{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/load-model","entry":"load_model","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":479,"n_papers_ran":59,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":501,"n_samples_ran":57,"n_samples_fingerprinted":1,"n_places":539,"n_places_pointer_only":193,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":16,"ran_fixture":0,"ran":39,"unverified":444},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2609.15530","paper":"/paper/arxiv-2609-15530","title":"Option-Aware Retrieval and Task-Specific VLM Adaptation for Medical VQA","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"Kirscher/MedReason2026","path":"finetune/train_lora_qwen25vl.py","file_url":"https://github.com/Kirscher/MedReason2026/blob/HEAD/finetune/train_lora_qwen25vl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"07f007e3a1985c92","mcp_get_code":{"code_sha256":"07f007e3a1985c92"}},{"arxiv_id":"2609.14316","paper":"/paper/arxiv-2609-14316","title":"LEARNING CONTINUOUS SOURCE RESPONSES FOR GENERALIZABLE AI-GENERATED IMAGE DETECTION","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"manic-cui/CuRe","path":"cure/model.py","file_url":"https://github.com/manic-cui/CuRe/blob/HEAD/cure/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b02a1d9300960243","mcp_get_code":{"code_sha256":"b02a1d9300960243"}},{"arxiv_id":"2609.14144","paper":"/paper/arxiv-2609-14144","title":"One Size Does Not Fit All Setting Inference Depth from the Questions a Deployment Actually Asks","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"jerrykaplan/question-conditioned-early-exit","path":"code/pipeline.py","file_url":"https://github.com/jerrykaplan/question-conditioned-early-exit/blob/HEAD/code/pipeline.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"afc32ea86d30bc31","mcp_get_code":{"code_sha256":"afc32ea86d30bc31"}},{"arxiv_id":"2609.01224","paper":"/paper/arxiv-2609-01224","title":"S$^2$Prune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"yuanyuanjia71-spec/S2Prune","path":"s2prune/qwen.py","file_url":"https://github.com/yuanyuanjia71-spec/S2Prune/blob/HEAD/s2prune/qwen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8cbe1d5962ac9dab","mcp_get_code":{"code_sha256":"8cbe1d5962ac9dab"}},{"arxiv_id":"2609.00591","paper":"/paper/arxiv-2609-00591","title":"A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"srynsh/SimLoss-Image-Captioning","path":"simloss/grpo_gemini/eval_checkpoint.py","file_url":"https://github.com/srynsh/SimLoss-Image-Captioning/blob/HEAD/simloss/grpo_gemini/eval_checkpoint.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3010b88e4be192d4","mcp_get_code":{"code_sha256":"3010b88e4be192d4"}},{"arxiv_id":"2608.31108","paper":"/paper/arxiv-2608-31108","title":"Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"VectorInstitute/sustainable-rai-evaluation","path":"src/evaluation_has_a_footprint/inference.py","file_url":"https://github.com/VectorInstitute/sustainable-rai-evaluation/blob/HEAD/src/evaluation_has_a_footprint/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac797b8a96bcf2ca","mcp_get_code":{"code_sha256":"ac797b8a96bcf2ca"}},{"arxiv_id":"2608.24952","paper":"/paper/arxiv-2608-24952","title":"The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"socialnlp/dialecttax","path":"src/dialecttax/gradients.py","file_url":"https://github.com/socialnlp/dialecttax/blob/HEAD/src/dialecttax/gradients.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c562479f4ac54069","mcp_get_code":{"code_sha256":"c562479f4ac54069"}},{"arxiv_id":"2608.24952","paper":"/paper/arxiv-2608-24952","title":"The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"socialnlp/dialecttax","path":"src/dialecttax/layers.py","file_url":"https://github.com/socialnlp/dialecttax/blob/HEAD/src/dialecttax/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec1366915e828453","mcp_get_code":{"code_sha256":"ec1366915e828453"}},{"arxiv_id":"2608.23358","paper":"/paper/arxiv-2608-23358","title":"The Geometry of Low-Resource Language Representations","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"francois-meyer/cpt-geometry","path":"compute_geometric_metrics.py","file_url":"https://github.com/francois-meyer/cpt-geometry/blob/HEAD/compute_geometric_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e5e67d5fee802c9","mcp_get_code":{"code_sha256":"6e5e67d5fee802c9"}},{"arxiv_id":"2608.18242","paper":"/paper/arxiv-2608-18242","title":"ClosureBench: Compositional Graph Reasoning ClosureBench: A Constructive Benchmark for Compositional Graph Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"egolabs-ai/closurebench","path":"src/closurebench/model_runner.py","file_url":"https://github.com/egolabs-ai/closurebench/blob/HEAD/src/closurebench/model_runner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e42dd549759b1b3","mcp_get_code":{"code_sha256":"2e42dd549759b1b3"}},{"arxiv_id":"2608.15073","paper":"/paper/arxiv-2608-15073","title":"BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"XZT008/Standard-GP-is-all-you-need-for-HDBO","path":"benchmark/nn_pruning_utils.py","file_url":"https://github.com/XZT008/Standard-GP-is-all-you-need-for-HDBO/blob/HEAD/benchmark/nn_pruning_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94bf24acd89a9223","mcp_get_code":{"code_sha256":"94bf24acd89a9223"}},{"arxiv_id":"2608.11922","paper":"/paper/arxiv-2608-11922","title":"LODESTAR: Robust Entropy-Based Answer Selection in Retrieval-Augmented Generation for Question Answering -- Directing Frozen-LLM Entropy with a Reinforcement-Learned Prompt Polarizer under Misleading Passages","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"zjysteven/mink-plus-plus","path":"run_concat.py","file_url":"https://github.com/zjysteven/mink-plus-plus/blob/HEAD/run_concat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ab747637b9f94fd","mcp_get_code":{"code_sha256":"4ab747637b9f94fd"}},{"arxiv_id":"2608.11922","paper":"/paper/arxiv-2608-11922","title":"LODESTAR: Robust Entropy-Based Answer Selection in Retrieval-Augmented Generation for Question Answering -- Directing Frozen-LLM Entropy with a Reinforcement-Learned Prompt Polarizer under Misleading Passages","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"zjysteven/mink-plus-plus","path":"run_neighbor.py","file_url":"https://github.com/zjysteven/mink-plus-plus/blob/HEAD/run_neighbor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16dffe12946ec141","mcp_get_code":{"code_sha256":"16dffe12946ec141"}},{"arxiv_id":"2608.11922","paper":"/paper/arxiv-2608-11922","title":"LODESTAR: Robust Entropy-Based Answer Selection in Retrieval-Augmented Generation for Question Answering -- Directing Frozen-LLM Entropy with a Reinforcement-Learned Prompt Polarizer under Misleading Passages","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"zjysteven/mink-plus-plus","path":"run_ref.py","file_url":"https://github.com/zjysteven/mink-plus-plus/blob/HEAD/run_ref.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d20c0c29841239c7","mcp_get_code":{"code_sha256":"d20c0c29841239c7"}},{"arxiv_id":"2608.11755","paper":"/paper/arxiv-2608-11755","title":"MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"WuqnEl/MuseCritic","path":"infer/infer.py","file_url":"https://github.com/WuqnEl/MuseCritic/blob/HEAD/infer/infer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"86f4dc3a1cb82f70","mcp_get_code":{"code_sha256":"86f4dc3a1cb82f70"}},{"arxiv_id":"2607.28306","paper":"/paper/arxiv-2607-28306","title":"A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"UAH-PSI/das-vessel-detection","path":"models/baseline_xgb_regression_model.py","file_url":"https://github.com/UAH-PSI/das-vessel-detection/blob/HEAD/models/baseline_xgb_regression_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"fa4e0107672fee04","mcp_get_code":{"code_sha256":"fa4e0107672fee04"}},{"arxiv_id":"2607.19510","paper":"/paper/arxiv-2607-19510","title":"Total Variation Distance Estimation in Autoregressive Models","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"XunZhiyang/llm-tv-estimation","path":"experiments/tv_estimate.py","file_url":"https://github.com/XunZhiyang/llm-tv-estimation/blob/HEAD/experiments/tv_estimate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"801eda14b08668a6","mcp_get_code":{"code_sha256":"801eda14b08668a6"}},{"arxiv_id":"2607.19317","paper":"/paper/arxiv-2607-19317","title":"Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Lexsi-Labs/CircuitKIT","path":"src/circuitkit/quick.py","file_url":"https://github.com/Lexsi-Labs/CircuitKIT/blob/HEAD/src/circuitkit/quick.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d493cdc4a900b3b5","mcp_get_code":{"code_sha256":"d493cdc4a900b3b5"}},{"arxiv_id":"2607.14509","paper":"/paper/arxiv-2607-14509","title":"Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"dsgt-arc/plantclef-2026","path":"user/murilo/src/plantclef/model.py","file_url":"https://github.com/dsgt-arc/plantclef-2026/blob/HEAD/user/murilo/src/plantclef/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bcb5e1827bc3fd86","mcp_get_code":{"code_sha256":"bcb5e1827bc3fd86"}},{"arxiv_id":"2606.30951","paper":"/paper/arxiv-2606-30951","title":"Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"DeepRCL/Prost-RL","path":"prostnfound/generate_clinical_plots.py","file_url":"https://github.com/DeepRCL/Prost-RL/blob/HEAD/prostnfound/generate_clinical_plots.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d90980e2dc9b0791","mcp_get_code":{"code_sha256":"d90980e2dc9b0791"}},{"arxiv_id":"2606.30951","paper":"/paper/arxiv-2606-30951","title":"Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"DeepRCL/Prost-RL","path":"prostnfound/inference.py","file_url":"https://github.com/DeepRCL/Prost-RL/blob/HEAD/prostnfound/inference.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f06764c411a6d202","mcp_get_code":{"code_sha256":"f06764c411a6d202"}},{"arxiv_id":"2606.22305","paper":"/paper/arxiv-2606-22305","title":"Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Richard-zrx/ADS","path":"src/model_utils.py","file_url":"https://github.com/Richard-zrx/ADS/blob/HEAD/src/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3ec62009af07711a","mcp_get_code":{"code_sha256":"3ec62009af07711a"}},{"arxiv_id":"2606.19184","paper":"/paper/arxiv-2606-19184","title":"When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"10Ring/LAA-Net","path":"models/utils.py","file_url":"https://github.com/10Ring/LAA-Net/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e59ae2c29ff112ff","mcp_get_code":{"code_sha256":"e59ae2c29ff112ff"}},{"arxiv_id":"2606.10537","paper":"/paper/arxiv-2606-10537","title":"Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"menik1126/Prefilling-dLLM","path":"prefilling_dllm_eval/prefilling_model.py","file_url":"https://github.com/menik1126/Prefilling-dLLM/blob/HEAD/prefilling_dllm_eval/prefilling_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f95d887a0d400a5","mcp_get_code":{"code_sha256":"7f95d887a0d400a5"}},{"arxiv_id":"2606.04473","paper":"/paper/arxiv-2606-04473","title":"ChessMimic: Per-Rating Transformer Models for Human Move, Clock, and Outcome Prediction in Online Blitz Chess","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"CSSLab/maia3","path":"maia3/uci.py","file_url":"https://github.com/CSSLab/maia3/blob/HEAD/maia3/uci.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"a92a6857d4a61de8","mcp_get_code":{"code_sha256":"a92a6857d4a61de8"}},{"arxiv_id":"2606.00082","paper":"/paper/arxiv-2606-00082","title":"Hoeffding Concept Bottleneck Models with Applications to Overhead Images","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"ericenouen/cbdebug","path":"pcbm/models/derma_models.py","file_url":"https://github.com/ericenouen/cbdebug/blob/HEAD/pcbm/models/derma_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfb5f5240ab7204c","mcp_get_code":{"code_sha256":"cfb5f5240ab7204c"}},{"arxiv_id":"2605.30675","paper":"/paper/arxiv-2605-30675","title":"Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"KyleAMoore/LLM-UQ-Align-and-Calibrate","path":"LinearProbing/ActivationGrabber.py","file_url":"https://github.com/KyleAMoore/LLM-UQ-Align-and-Calibrate/blob/HEAD/LinearProbing/ActivationGrabber.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6012bb03063365be","mcp_get_code":{"code_sha256":"6012bb03063365be"}},{"arxiv_id":"2605.28227","paper":"/paper/arxiv-2605-28227","title":"Why We Need Speech to Evaluate Speech Translation","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"MaikeZuefle/speechCOMET","path":"speechcomet-eval/eval_utils.py","file_url":"https://github.com/MaikeZuefle/speechCOMET/blob/HEAD/speechcomet-eval/eval_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3fa3ad39c2c36c62","mcp_get_code":{"code_sha256":"3fa3ad39c2c36c62"}},{"arxiv_id":"2605.26315","paper":"/paper/arxiv-2605-26315","title":"Curriculum Learning for Safety Alignment","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Sandeep5500/curriculum-learning-for-safety","path":"src/interpretability/probe_safety.py","file_url":"https://github.com/Sandeep5500/curriculum-learning-for-safety/blob/HEAD/src/interpretability/probe_safety.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"28a23205d560ec6c","mcp_get_code":{"code_sha256":"28a23205d560ec6c"}},{"arxiv_id":"2605.24042","paper":"/paper/arxiv-2605-24042","title":"Hidden-State Privacy Has an Empty Middle","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"okezue/tcc-research","path":"two_channel/compute_subspace.py","file_url":"https://github.com/okezue/tcc-research/blob/HEAD/two_channel/compute_subspace.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5148fb5c61d1921e","mcp_get_code":{"code_sha256":"5148fb5c61d1921e"}},{"arxiv_id":"2605.23918","paper":"/paper/arxiv-2605-23918","title":"The Model Parking Tax: Quantifying the Hidden Energy Cost of Always-On GPU Model Deployment","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"8bitai/gpu-parking-tax","path":"experiments/model_validation.py","file_url":"https://github.com/8bitai/gpu-parking-tax/blob/HEAD/experiments/model_validation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"26577c1c91197f36","mcp_get_code":{"code_sha256":"26577c1c91197f36"}},{"arxiv_id":"2605.22644","paper":"/paper/arxiv-2605-22644","title":"Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"brain-lab-research/SGDiffusion","path":"src/datamodelopt/core/checkpointing.py","file_url":"https://github.com/brain-lab-research/SGDiffusion/blob/HEAD/src/datamodelopt/core/checkpointing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"645eff8e04b31c3e","mcp_get_code":{"code_sha256":"645eff8e04b31c3e"}},{"arxiv_id":"2605.20075","paper":"/paper/arxiv-2605-20075","title":"CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"shishirpatil/gorilla","path":"gorilla/inference/gorilla_eval.py","file_url":"https://github.com/shishirpatil/gorilla/blob/HEAD/gorilla/inference/gorilla_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ba873c8d7fc8d9c3","mcp_get_code":{"code_sha256":"ba873c8d7fc8d9c3"}},{"arxiv_id":"2605.19639","paper":"/paper/arxiv-2605-19639","title":"Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"xiaomoguhz/R3-Bench","path":"r3bench/models/qwen2_5vl.py","file_url":"https://github.com/xiaomoguhz/R3-Bench/blob/HEAD/r3bench/models/qwen2_5vl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9ce1684c2b4e4437","mcp_get_code":{"code_sha256":"9ce1684c2b4e4437"}},{"arxiv_id":"2605.18053","paper":"/paper/arxiv-2605-18053","title":"Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"gpgabriel25/KVCacheBoundaryProtection","path":"src/counterfact_kv_eviction/jax_inference.py","file_url":"https://github.com/gpgabriel25/KVCacheBoundaryProtection/blob/HEAD/src/counterfact_kv_eviction/jax_inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7ce53772f6a98f99","mcp_get_code":{"code_sha256":"7ce53772f6a98f99"}},{"arxiv_id":"2605.13981","paper":"/paper/arxiv-2605-13981","title":"Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"StellarLuminosity/Energy","path":"distill_bench/pipelines/kd_eval.py","file_url":"https://github.com/StellarLuminosity/Energy/blob/HEAD/distill_bench/pipelines/kd_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"99c76a090a976891","mcp_get_code":{"code_sha256":"99c76a090a976891"}},{"arxiv_id":"2605.11396","paper":"/paper/arxiv-2605-11396","title":"MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"YupengSu/MuonQ","path":"src/model.py","file_url":"https://github.com/YupengSu/MuonQ/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e8a2ded649151f29","mcp_get_code":{"code_sha256":"e8a2ded649151f29"}},{"arxiv_id":"2605.08765","paper":"/paper/arxiv-2605-08765","title":"Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"OPTML-Group/ReVa","path":"src/train/reva/utils.py","file_url":"https://github.com/OPTML-Group/ReVa/blob/HEAD/src/train/reva/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7bdf184a0ba57b39","mcp_get_code":{"code_sha256":"7bdf184a0ba57b39"}},{"arxiv_id":"2605.06870","paper":"/paper/arxiv-2605-06870","title":"Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"xyz-zy/vqvae_latent_span_collapse","path":"VQGAN/eval_val_std.py","file_url":"https://github.com/xyz-zy/vqvae_latent_span_collapse/blob/HEAD/VQGAN/eval_val_std.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8836f887cef4509","mcp_get_code":{"code_sha256":"a8836f887cef4509"}},{"arxiv_id":"2604.15373","paper":"/paper/arxiv-2604-15373","title":"InfoChess: A Game of Adversarial Inference and a Laboratory for Quantifiable Information Control","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"murphyka/infochess","path":"agents/hiding_belief_vismax_agent.py","file_url":"https://github.com/murphyka/infochess/blob/HEAD/agents/hiding_belief_vismax_agent.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dac3e8f15d9c31a","mcp_get_code":{"code_sha256":"4dac3e8f15d9c31a"}},{"arxiv_id":"2604.14602","paper":"/paper/arxiv-2604-14602","title":"CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"unitaryai/detoxify","path":"detoxify/detoxify.py","file_url":"https://github.com/unitaryai/detoxify/blob/HEAD/detoxify/detoxify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"784c42a453ac5e68","mcp_get_code":{"code_sha256":"784c42a453ac5e68"}},{"arxiv_id":"2604.12493","paper":"/paper/arxiv-2604-12493","title":"Latent Planning Emerges with Scale","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"hannamw/model-planning-public","path":"animal_stories/evaluate_animal_stories.py","file_url":"https://github.com/hannamw/model-planning-public/blob/HEAD/animal_stories/evaluate_animal_stories.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09fd84d63d8f2a44","mcp_get_code":{"code_sha256":"09fd84d63d8f2a44"}},{"arxiv_id":"2604.12016","paper":"/paper/arxiv-2604-12016","title":"Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"b102e/yar-attractor-experiment","path":"ablation_experiment/extract_activations.py","file_url":"https://github.com/b102e/yar-attractor-experiment/blob/HEAD/ablation_experiment/extract_activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f87471a2e2a3504","mcp_get_code":{"code_sha256":"6f87471a2e2a3504"}},{"arxiv_id":"2604.07035","paper":"/paper/arxiv-2604-07035","title":"Unified Deployment-Aware Evaluation of Open Reasoning Language Models","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"mkboch/UDAE","path":"models/loader.py","file_url":"https://github.com/mkboch/UDAE/blob/HEAD/models/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6b7c1e512fe35435","mcp_get_code":{"code_sha256":"6b7c1e512fe35435"}},{"arxiv_id":"2603.25687","paper":"/paper/arxiv-2603-25687","title":"On Neural Scaling Laws for Weather Emulation through Continual Training","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"ShashankSubramanian/neural-scaling-weather","path":"models/helpers.py","file_url":"https://github.com/ShashankSubramanian/neural-scaling-weather/blob/HEAD/models/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6de4ed472a68af2e","mcp_get_code":{"code_sha256":"6de4ed472a68af2e"}},{"arxiv_id":"2603.20640","paper":"/paper/arxiv-2603-20640","title":"Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"DA2I2-SLM/DAR","path":"src/model/falcon.py","file_url":"https://github.com/DA2I2-SLM/DAR/blob/HEAD/src/model/falcon.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83f5d8fd4615419d","mcp_get_code":{"code_sha256":"83f5d8fd4615419d"}},{"arxiv_id":"2603.20640","paper":"/paper/arxiv-2603-20640","title":"Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"DA2I2-SLM/DAR","path":"src/model/llama.py","file_url":"https://github.com/DA2I2-SLM/DAR/blob/HEAD/src/model/llama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8d90d67f4aad3ad","mcp_get_code":{"code_sha256":"f8d90d67f4aad3ad"}},{"arxiv_id":"2603.15546","paper":"/paper/arxiv-2603-15546","title":"Kimodo: Scaling Controllable Human Motion Generation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"nv-tlabs/kimodo","path":"kimodo/model/load_model.py","file_url":"https://github.com/nv-tlabs/kimodo/blob/HEAD/kimodo/model/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9864a44c22701ca7","mcp_get_code":{"code_sha256":"9864a44c22701ca7"}},{"arxiv_id":"2603.14254","paper":"/paper/arxiv-2603-14254","title":"ZOTTA: Test-Time Adaptation with Gradient-Free Zeroth-Order Optimization","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"Zhang-Ronghao/zotta","path":"tta_library/zotta.py","file_url":"https://github.com/Zhang-Ronghao/zotta/blob/HEAD/tta_library/zotta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b969394a74a96c4","mcp_get_code":{"code_sha256":"7b969394a74a96c4"}},{"arxiv_id":"2603.13893","paper":"/paper/arxiv-2603-13893","title":"UVLM: A Universal Vision-Language Model Loader for Reproducible Multimodal Benchmarking","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"perezjoan/UVLM","path":"uvlm/loader.py","file_url":"https://github.com/perezjoan/UVLM/blob/HEAD/uvlm/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0ac9db5392bf70d0","mcp_get_code":{"code_sha256":"0ac9db5392bf70d0"}},{"arxiv_id":"2603.11327","paper":"/paper/arxiv-2603-11327","title":"Meta-Reinforcement Learning with Self-Reflection for Agentic Search","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"tengxiao1/MR-Search","path":"meta-search/search/index_builder.py","file_url":"https://github.com/tengxiao1/MR-Search/blob/HEAD/meta-search/search/index_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4260fe0918ac5196","mcp_get_code":{"code_sha256":"4260fe0918ac5196"}},{"arxiv_id":"2603.01923","paper":"/paper/arxiv-2603-01923","title":"Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"ronaldogomes96/explication-ann","path":"src/models/utils.py","file_url":"https://github.com/ronaldogomes96/explication-ann/blob/HEAD/src/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5fd51d1a8831f916","mcp_get_code":{"code_sha256":"5fd51d1a8831f916"}},{"arxiv_id":"2602.22278","paper":"/paper/arxiv-2602-22278","title":"RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"alivecat05/RETLLM","path":"main_eval.py","file_url":"https://github.com/alivecat05/RETLLM/blob/HEAD/main_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e4ea555a8a80225","mcp_get_code":{"code_sha256":"3e4ea555a8a80225"}},{"arxiv_id":"2602.19253","paper":"/paper/arxiv-2602-19253","title":"Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"QusaiKhaled/XANFIS","path":"visualizer.py","file_url":"https://github.com/QusaiKhaled/XANFIS/blob/HEAD/visualizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2387b8a3f0a8bbce","mcp_get_code":{"code_sha256":"2387b8a3f0a8bbce"}},{"arxiv_id":"2602.17653","paper":"/paper/arxiv-2602-17653","title":"Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Iskar-Deng/DAM-learning","path":"evaluation/eval_minpairs_acc.py","file_url":"https://github.com/Iskar-Deng/DAM-learning/blob/HEAD/evaluation/eval_minpairs_acc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0af832420874c823","mcp_get_code":{"code_sha256":"0af832420874c823"}},{"arxiv_id":"2602.10635","paper":"/paper/arxiv-2602-10635","title":"OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"MIT-MI/human_behavior_atlas","path":"training/sft/train_sft.py","file_url":"https://github.com/MIT-MI/human_behavior_atlas/blob/HEAD/training/sft/train_sft.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7380e01e3cd064c2","mcp_get_code":{"code_sha256":"7380e01e3cd064c2"}},{"arxiv_id":"2601.21283","paper":"/paper/arxiv-2601-21283","title":"DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"EasonZhong99/DUET","path":"eval_mmlu.py","file_url":"https://github.com/EasonZhong99/DUET/blob/HEAD/eval_mmlu.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa11352c89b1d959","mcp_get_code":{"code_sha256":"fa11352c89b1d959"}},{"arxiv_id":"2601.21283","paper":"/paper/arxiv-2601-21283","title":"DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"EasonZhong99/DUET","path":"eval_qa_rouge.py","file_url":"https://github.com/EasonZhong99/DUET/blob/HEAD/eval_qa_rouge.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"43ca23f94d7ca39e","mcp_get_code":{"code_sha256":"43ca23f94d7ca39e"}},{"arxiv_id":"2601.20753","paper":"/paper/arxiv-2601-20753","title":"GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"jzh001/GraphAllocBench","path":"graphallocbench/evaluation/model_utils.py","file_url":"https://github.com/jzh001/GraphAllocBench/blob/HEAD/graphallocbench/evaluation/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fde5af6e100cd769","mcp_get_code":{"code_sha256":"fde5af6e100cd769"}},{"arxiv_id":"2601.19449","paper":"/paper/arxiv-2601-19449","title":"Fixed Aggregation Features Can Rival GNNs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"celrm/fixed-aggregation-features","path":"logger.py","file_url":"https://github.com/celrm/fixed-aggregation-features/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d8e306910a720c14","mcp_get_code":{"code_sha256":"d8e306910a720c14"}},{"arxiv_id":"2601.18897","paper":"/paper/arxiv-2601-18897","title":"Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"QusaiKhaled/XUQ","path":"src/utils/persistence.py","file_url":"https://github.com/QusaiKhaled/XUQ/blob/HEAD/src/utils/persistence.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b239de6c32d9aae","mcp_get_code":{"code_sha256":"0b239de6c32d9aae"}},{"arxiv_id":"2601.18555","paper":"/paper/arxiv-2601-18555","title":"A CROSS-MODALITY VALIDATION OF MRI VERSUS X-RAY","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Malga-Vision/Landmarks-Hip-Conditions","path":"deep_learning.py","file_url":"https://github.com/Malga-Vision/Landmarks-Hip-Conditions/blob/HEAD/deep_learning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4675b09039b1e3f2","mcp_get_code":{"code_sha256":"4675b09039b1e3f2"}},{"arxiv_id":"2601.17950","paper":"/paper/arxiv-2601-17950","title":"UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"mwalmer-umd/UPLiFT","path":"uplift/hub_loader.py","file_url":"https://github.com/mwalmer-umd/UPLiFT/blob/HEAD/uplift/hub_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80db0b2e7f2f1d55","mcp_get_code":{"code_sha256":"80db0b2e7f2f1d55"}},{"arxiv_id":"2601.17197","paper":"/paper/arxiv-2601-17197","title":"Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"scheshmi/CrossStyle-MMR","path":"infer/binary.py","file_url":"https://github.com/scheshmi/CrossStyle-MMR/blob/HEAD/infer/binary.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"59edd20c4877c02d","mcp_get_code":{"code_sha256":"59edd20c4877c02d"}},{"arxiv_id":"2601.17197","paper":"/paper/arxiv-2601-17197","title":"Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"scheshmi/CrossStyle-MMR","path":"infer/deepseek.py","file_url":"https://github.com/scheshmi/CrossStyle-MMR/blob/HEAD/infer/deepseek.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3c8e93cd926f0c75","mcp_get_code":{"code_sha256":"3c8e93cd926f0c75"}},{"arxiv_id":"2601.17197","paper":"/paper/arxiv-2601-17197","title":"Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"scheshmi/CrossStyle-MMR","path":"infer/sft_cot.py","file_url":"https://github.com/scheshmi/CrossStyle-MMR/blob/HEAD/infer/sft_cot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6b5634816c6b7c0","mcp_get_code":{"code_sha256":"e6b5634816c6b7c0"}},{"arxiv_id":"2601.16644","paper":"/paper/arxiv-2601-16644","title":"Sycophancy Hides Linearly in the Attention Heads","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"rifoagenadi/sycophancy","path":"utils.py","file_url":"https://github.com/rifoagenadi/sycophancy/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"04576b9fbc4350fb","mcp_get_code":{"code_sha256":"04576b9fbc4350fb"}},{"arxiv_id":"2601.16644","paper":"/paper/arxiv-2601-16644","title":"Sycophancy Hides Linearly in the Attention Heads","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"rifoagenadi/sycophancy","path":"extension/extract_activations.py","file_url":"https://github.com/rifoagenadi/sycophancy/blob/HEAD/extension/extract_activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5c3f8ddf9c8f027","mcp_get_code":{"code_sha256":"c5c3f8ddf9c8f027"}},{"arxiv_id":"2601.15429","paper":"/paper/arxiv-2601-15429","title":"Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"sydneyanuyah/RAGComparison","path":"artifacts/complex_simplify_sentences.py","file_url":"https://github.com/sydneyanuyah/RAGComparison/blob/HEAD/artifacts/complex_simplify_sentences.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f242630adcccd143","mcp_get_code":{"code_sha256":"f242630adcccd143"}},{"arxiv_id":"2601.14896","paper":"/paper/arxiv-2601-14896","title":"Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Cherry-qwq/LcRL-Open","path":"search_r1/search/index_builder.py","file_url":"https://github.com/Cherry-qwq/LcRL-Open/blob/HEAD/search_r1/search/index_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4260fe0918ac5196","mcp_get_code":{"code_sha256":"4260fe0918ac5196"}},{"arxiv_id":"2601.12079","paper":"/paper/arxiv-2601-12079","title":"EmoLat: Text-driven Image Sentiment Transfer via Emotion Latent Space","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"JingVIPLab/EmoLat","path":"emo_token_gen_model/emo_token_gen_model.py","file_url":"https://github.com/JingVIPLab/EmoLat/blob/HEAD/emo_token_gen_model/emo_token_gen_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"052d17d995e09058","mcp_get_code":{"code_sha256":"052d17d995e09058"}},{"arxiv_id":"2601.10712","paper":"/paper/arxiv-2601-10712","title":"MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"bytedance/FTRL","path":"Code/utils/utils.py","file_url":"https://github.com/bytedance/FTRL/blob/HEAD/Code/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ec450fb84a121b33","mcp_get_code":{"code_sha256":"ec450fb84a121b33"}},{"arxiv_id":"2601.02443","paper":"/paper/arxiv-2601-02443","title":"Evaluating the Diagnostic Classification Ability of Multimodal Large Language Models: Insights from the Osteoarthritis Initiative","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"wanglihx/LLaVA-OA","path":"3-clip/clipeval.py","file_url":"https://github.com/wanglihx/LLaVA-OA/blob/HEAD/3-clip/clipeval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a7a6d2fd4d5b010b","mcp_get_code":{"code_sha256":"a7a6d2fd4d5b010b"}},{"arxiv_id":"2601.01552","paper":"/paper/arxiv-2601-01552","title":"HalluZig: Hallucination Detection using Zigzag Persistence","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"TDA-Jyamiti/halluzig","path":"utils.py","file_url":"https://github.com/TDA-Jyamiti/halluzig/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b09843d4d3ac9742","mcp_get_code":{"code_sha256":"b09843d4d3ac9742"}},{"arxiv_id":"2512.14395","paper":"/paper/arxiv-2512-14395","title":"Massive Editing for Large Language Models Based on Dynamic Weight Generation","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"RodeWayne/MeG-for-Knowledge-Editing","path":"myDataloader_bert_text_add_nq_lr.py","file_url":"https://github.com/RodeWayne/MeG-for-Knowledge-Editing/blob/HEAD/myDataloader_bert_text_add_nq_lr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f81834e47a60a64","mcp_get_code":{"code_sha256":"5f81834e47a60a64"}},{"arxiv_id":"2512.14395","paper":"/paper/arxiv-2512-14395","title":"Massive Editing for Large Language Models Based on Dynamic Weight Generation","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"RodeWayne/MeG-for-Knowledge-Editing","path":"myDataloader_cf_bert_text_add_nq_lr.py","file_url":"https://github.com/RodeWayne/MeG-for-Knowledge-Editing/blob/HEAD/myDataloader_cf_bert_text_add_nq_lr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a80e760cc60c9b9","mcp_get_code":{"code_sha256":"4a80e760cc60c9b9"}},{"arxiv_id":"2512.11574","paper":"/paper/arxiv-2512-11574","title":"Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"ToyeshC/open-hummingbird-3d-eval","path":"python_scripts/run_eval_4_table_reproduction.py","file_url":"https://github.com/ToyeshC/open-hummingbird-3d-eval/blob/HEAD/python_scripts/run_eval_4_table_reproduction.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"376edd4d5015083b","mcp_get_code":{"code_sha256":"376edd4d5015083b"}},{"arxiv_id":"2512.11574","paper":"/paper/arxiv-2512-11574","title":"Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"ToyeshC/open-hummingbird-3d-eval","path":"python_scripts/old_exp_b.py","file_url":"https://github.com/ToyeshC/open-hummingbird-3d-eval/blob/HEAD/python_scripts/old_exp_b.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b0243bb299195f2","mcp_get_code":{"code_sha256":"0b0243bb299195f2"}},{"arxiv_id":"2512.08896","paper":"/paper/arxiv-2512-08896","title":"Open Polymer Challenge: Post-Competition Report","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"sobinalosious/ADEPT","path":"3.Analysis/predict_tg.py","file_url":"https://github.com/sobinalosious/ADEPT/blob/HEAD/3.Analysis/predict_tg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dd3d61d82c74d5f1","mcp_get_code":{"code_sha256":"dd3d61d82c74d5f1"}},{"arxiv_id":"2510.18825","paper":"/paper/arxiv-2510-18825","title":"Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"null-xyj/M3Dphormer","path":"logger.py","file_url":"https://github.com/null-xyj/M3Dphormer/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36604764af0f2cb1","mcp_get_code":{"code_sha256":"36604764af0f2cb1"}},{"arxiv_id":"2510.17425","paper":"/paper/arxiv-2510-17425","title":"Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"booktrackerGirl/climate_change_policy_analysis","path":"src/summarizer_script.py","file_url":"https://github.com/booktrackerGirl/climate_change_policy_analysis/blob/HEAD/src/summarizer_script.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77abb22b27773143","mcp_get_code":{"code_sha256":"77abb22b27773143"}},{"arxiv_id":"2510.16877","paper":"/paper/arxiv-2510-16877","title":"Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"gfyddha/Fly-CL","path":"models/load_model.py","file_url":"https://github.com/gfyddha/Fly-CL/blob/HEAD/models/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33865bb75490b774","mcp_get_code":{"code_sha256":"33865bb75490b774"}},{"arxiv_id":"2510.15963","paper":"/paper/arxiv-2510-15963","title":"ESCA: Contextualizing Embodied Agents via Scene-Graph Generation","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"video-fm/ESCA","path":"embodiedbench/planner/navigation/esca.py","file_url":"https://github.com/video-fm/ESCA/blob/HEAD/embodiedbench/planner/navigation/esca.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4e8a6e34592c45a","mcp_get_code":{"code_sha256":"d4e8a6e34592c45a"}},{"arxiv_id":"2510.05566","paper":"/paper/arxiv-2510-05566","title":"Domain-Shift-Aware Conformal Prediction for Large Language Models","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"smartyfh/LLM-Uncertainty-Bench","path":"generate_logits.py","file_url":"https://github.com/smartyfh/LLM-Uncertainty-Bench/blob/HEAD/generate_logits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35ff91b4ff9b46ab","mcp_get_code":{"code_sha256":"35ff91b4ff9b46ab"}},{"arxiv_id":"2510.05566","paper":"/paper/arxiv-2510-05566","title":"Domain-Shift-Aware Conformal Prediction for Large Language Models","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"smartyfh/LLM-Uncertainty-Bench","path":"generate_logits_chat.py","file_url":"https://github.com/smartyfh/LLM-Uncertainty-Bench/blob/HEAD/generate_logits_chat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba6a8351d724ca74","mcp_get_code":{"code_sha256":"ba6a8351d724ca74"}},{"arxiv_id":"2509.09651","paper":"/paper/arxiv-2509-09651","title":"Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"Zakaria010/Radio-RAG","path":"local_rag.py","file_url":"https://github.com/Zakaria010/Radio-RAG/blob/HEAD/local_rag.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f60f347c4394a7a","mcp_get_code":{"code_sha256":"6f60f347c4394a7a"}},{"arxiv_id":"2508.17536","paper":"/paper/arxiv-2508-17536","title":"Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"deeplearning-wisc/debate-or-vote","path":"src/model/llama.py","file_url":"https://github.com/deeplearning-wisc/debate-or-vote/blob/HEAD/src/model/llama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d37a4d52cbb58ff4","mcp_get_code":{"code_sha256":"d37a4d52cbb58ff4"}},{"arxiv_id":"2508.17536","paper":"/paper/arxiv-2508-17536","title":"Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"deeplearning-wisc/debate-or-vote","path":"src/model/qwen.py","file_url":"https://github.com/deeplearning-wisc/debate-or-vote/blob/HEAD/src/model/qwen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5eac478ca93897a7","mcp_get_code":{"code_sha256":"5eac478ca93897a7"}},{"arxiv_id":"2507.03167","paper":"/paper/adversarial-manipulation-of-reasoning-models","title":"Adversarial Manipulation of Reasoning Models using Internal Representations","date":"2025-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ky295/reasoning-manipulation","path":"interventions/create_ortho_model.py","file_url":"https://github.com/ky295/reasoning-manipulation/blob/HEAD/interventions/create_ortho_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4f29e94e48d679d","mcp_get_code":{"code_sha256":"d4f29e94e48d679d"}},{"arxiv_id":"2507.03167","paper":"/paper/adversarial-manipulation-of-reasoning-models","title":"Adversarial Manipulation of Reasoning Models using Internal Representations","date":"2025-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ky295/reasoning-manipulation","path":"interventions/activation_addition.py","file_url":"https://github.com/ky295/reasoning-manipulation/blob/HEAD/interventions/activation_addition.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2410d0fa9fb4d711","mcp_get_code":{"code_sha256":"2410d0fa9fb4d711"}},{"arxiv_id":"2506.20941","paper":"/paper/model-state-arithmetic-for-machine-unlearning","title":"Model State Arithmetic for Machine Unlearning","date":"2025-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mehrdadsaberi/msa_unlearning","path":"src/tv_unlearn.py","file_url":"https://github.com/mehrdadsaberi/msa_unlearning/blob/HEAD/src/tv_unlearn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fba3196222b060a3","mcp_get_code":{"code_sha256":"fba3196222b060a3"}},{"arxiv_id":"2506.18434","paper":null,"title":"arXiv:2506.18434","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"fruffini/PEFT_Prognosis","path":"src/utils/save_load.py","file_url":"https://github.com/fruffini/PEFT_Prognosis/blob/HEAD/src/utils/save_load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"83b037cd6a2822a5","mcp_get_code":{"code_sha256":"83b037cd6a2822a5"}},{"arxiv_id":"2506.13750","paper":"/paper/test3r-learning-to-reconstruct-3d-at-test","title":"Test3R: Learning to Reconstruct 3D at Test Time","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nopqaq/test3r","path":"dust3r/model.py","file_url":"https://github.com/nopqaq/test3r/blob/HEAD/dust3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1e3f140b86bb9a08","mcp_get_code":{"code_sha256":"1e3f140b86bb9a08"}},{"arxiv_id":"2505.23416","paper":"/paper/kvzip-query-agnostic-kv-cache-compression","title":"KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/kvzip","path":"model/load.py","file_url":"https://github.com/snu-mllab/kvzip/blob/HEAD/model/load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c76a69893a315be","mcp_get_code":{"code_sha256":"9c76a69893a315be"}},{"arxiv_id":"2505.21906","paper":"/paper/vision-language-action-model-with-open-world","title":"ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge","date":"2025-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tutujingyugang1/ChatVLA_public","path":"qwen2_vla/model_load_utils.py","file_url":"https://github.com/tutujingyugang1/ChatVLA_public/blob/HEAD/qwen2_vla/model_load_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e298c3ed316c8dcb","mcp_get_code":{"code_sha256":"e298c3ed316c8dcb"}},{"arxiv_id":"2505.19820","paper":"/paper/infocons-identifying-interpretable-critical","title":"InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llffff/infocons-pc","path":"code/vis.py","file_url":"https://github.com/llffff/infocons-pc/blob/HEAD/code/vis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6b2e031c74a89cd1","mcp_get_code":{"code_sha256":"6b2e031c74a89cd1"}},{"arxiv_id":"2505.19820","paper":"/paper/infocons-identifying-interpretable-critical","title":"InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llffff/infocons-pc","path":"code/test_dgcnn.py","file_url":"https://github.com/llffff/infocons-pc/blob/HEAD/code/test_dgcnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"918da5cdc787eff9","mcp_get_code":{"code_sha256":"918da5cdc787eff9"}},{"arxiv_id":"2505.19820","paper":"/paper/infocons-identifying-interpretable-critical","title":"InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llffff/infocons-pc","path":"code/test_curvenet.py","file_url":"https://github.com/llffff/infocons-pc/blob/HEAD/code/test_curvenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef4599d55ac1fe73","mcp_get_code":{"code_sha256":"ef4599d55ac1fe73"}},{"arxiv_id":"2505.19684","paper":"/paper/viscra-a-visual-chain-reasoning-attack-for","title":"VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DyMessi/VisCRA","path":"qwenmask.py","file_url":"https://github.com/DyMessi/VisCRA/blob/HEAD/qwenmask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"317d970e6d4be3bc","mcp_get_code":{"code_sha256":"317d970e6d4be3bc"}},{"arxiv_id":"2505.15559","paper":"/paper/moonbeam-a-midi-foundation-model-using-both","title":"Moonbeam: A MIDI Foundation Model Using Both Absolute and Relative Music Attributes","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guozixunnicolas/Moonbeam-MIDI-Foundation-Model","path":"src/llama_recipes/inference/model_utils.py","file_url":"https://github.com/guozixunnicolas/Moonbeam-MIDI-Foundation-Model/blob/HEAD/src/llama_recipes/inference/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6533a38ad979d2b6","mcp_get_code":{"code_sha256":"6533a38ad979d2b6"}},{"arxiv_id":"2505.15367","paper":"/paper/better-safe-than-sorry-overreaction-problem","title":"Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Dasol-Choi/VERI-Emergency","path":"src/q1_evaluation.py","file_url":"https://github.com/Dasol-Choi/VERI-Emergency/blob/HEAD/src/q1_evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55325907ea1100df","mcp_get_code":{"code_sha256":"55325907ea1100df"}},{"arxiv_id":"2505.03912","paper":"/paper/openhelix-a-short-survey-empirical-analysis","title":"OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation","date":"2025-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenHelix-robot/OpenHelix","path":"data_preprocessing/preprocess_calvin_instructions.py","file_url":"https://github.com/OpenHelix-robot/OpenHelix/blob/HEAD/data_preprocessing/preprocess_calvin_instructions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eb3169facaa8376","mcp_get_code":{"code_sha256":"1eb3169facaa8376"}},{"arxiv_id":"2504.11054","paper":"/paper/zero-shot-whole-body-humanoid-control-via","title":"Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models","date":"2025-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/metamotivo","path":"metamotivo/fb/model.py","file_url":"https://github.com/facebookresearch/metamotivo/blob/HEAD/metamotivo/fb/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"feddcd5c2a9fd9a2","mcp_get_code":{"code_sha256":"feddcd5c2a9fd9a2"}},{"arxiv_id":"2504.02160","paper":"/paper/less-to-more-generalization-unlocking-more","title":"Less-to-More Generalization: Unlocking More Controllability by In-Context Generation","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/UNO","path":"uno/flux/pipeline.py","file_url":"https://github.com/bytedance/UNO/blob/HEAD/uno/flux/pipeline.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5317947c40cdbb83","mcp_get_code":{"code_sha256":"5317947c40cdbb83"}},{"arxiv_id":"2503.22152","paper":"/paper/egotom-benchmarking-theory-of-mind-reasoning","title":"EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric Videos","date":"2025-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/egotom","path":"code/vlm_evaluate.py","file_url":"https://github.com/facebookresearch/egotom/blob/HEAD/code/vlm_evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"30a7dbbd7f93015f","mcp_get_code":{"code_sha256":"30a7dbbd7f93015f"}},{"arxiv_id":"2503.16356","paper":"/paper/cake-circuit-aware-editing-enables","title":"CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/CaKE","path":"Analysis/utils.py","file_url":"https://github.com/zjunlp/CaKE/blob/HEAD/Analysis/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb42f5ad478b4dbf","mcp_get_code":{"code_sha256":"bb42f5ad478b4dbf"}},{"arxiv_id":"2503.03687","paper":"/paper/addressing-overprescribing-challenges-fine","title":"Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation Tasks","date":"2025-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzhustc2016/lamo","path":"utils/model_utils.py","file_url":"https://github.com/zzhustc2016/lamo/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d6dbc80a8beff531","mcp_get_code":{"code_sha256":"d6dbc80a8beff531"}},{"arxiv_id":"2503.00522","paper":"/paper/periodic-materials-generation-using-text","title":"Periodic Materials Generation using Text-Guided Joint Diffusion Model","date":"2025-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdmsit/TGDMat","path":"csp_task/eval_utils.py","file_url":"https://github.com/kdmsit/TGDMat/blob/HEAD/csp_task/eval_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"415e6a383e6ff70c","mcp_get_code":{"code_sha256":"415e6a383e6ff70c"}},{"arxiv_id":"2502.20479","paper":"/paper/leveraging-pre-trained-visual-transformers","title":"Leveraging Pre-Trained Visual Transformers for Multi-Band Photometric Light Curve Classification","date":null,"month_inferred_from_arxiv_id":"2025-02","title_source":"archive","repo":"dnlmoreno/VT_Model_for_LightCurves_Classification","path":"scripts/run_online.py","file_url":"https://github.com/dnlmoreno/VT_Model_for_LightCurves_Classification/blob/HEAD/scripts/run_online.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ff0dc1588120ada4","mcp_get_code":{"code_sha256":"ff0dc1588120ada4"}},{"arxiv_id":"2502.20122","paper":"/paper/self-training-elicits-concise-reasoning-in","title":"Self-Training Elicits Concise Reasoning in Large Language Models","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tergelmunkhbat/concise-reasoning","path":"src/model.py","file_url":"https://github.com/tergelmunkhbat/concise-reasoning/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab895046a21a97a7","mcp_get_code":{"code_sha256":"ab895046a21a97a7"}},{"arxiv_id":"2502.16971","paper":"/paper/longsafety-evaluating-long-context-safety-of","title":"LongSafety: Evaluating Long-Context Safety of Large Language Models","date":"2025-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-coai/LongSafety","path":"src/gen_model_response.py","file_url":"https://github.com/thu-coai/LongSafety/blob/HEAD/src/gen_model_response.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f8345e3b106eff8","mcp_get_code":{"code_sha256":"7f8345e3b106eff8"}},{"arxiv_id":"2502.16690","paper":"/paper/from-text-to-space-mapping-abstract-spatial","title":"From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task","date":"2025-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mneuronico/griw-world-spatial-orientation-task","path":"experiments/fns.py","file_url":"https://github.com/mneuronico/griw-world-spatial-orientation-task/blob/HEAD/experiments/fns.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"038dc68a58ff4bb6","mcp_get_code":{"code_sha256":"038dc68a58ff4bb6"}},{"arxiv_id":"2502.11469","paper":"/paper/if-attention-serves-as-a-cognitive-model-of","title":"If Attention Serves as a Cognitive Model of Human Memory Retrieval, What is the Plausible Memory Representation?","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aistairc/rnng-pytorch","path":"beam_search.py","file_url":"https://github.com/aistairc/rnng-pytorch/blob/HEAD/beam_search.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b066f480d7472db","mcp_get_code":{"code_sha256":"1b066f480d7472db"}},{"arxiv_id":"2502.09838","paper":"/paper/healthgpt-a-medical-large-vision-language","title":"HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation","date":"2025-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dcdmllm/healthgpt","path":"HealthGPT-Pro/inference/utils.py","file_url":"https://github.com/dcdmllm/healthgpt/blob/HEAD/HealthGPT-Pro/inference/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"750616b8b0408b73","mcp_get_code":{"code_sha256":"750616b8b0408b73"}},{"arxiv_id":"2502.07557","paper":"/paper/jbshield-defending-large-language-models-from","title":"JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation","date":null,"month_inferred_from_arxiv_id":"2025-02","title_source":"archive","repo":"NISPLab/JBShield","path":"utils.py","file_url":"https://github.com/NISPLab/JBShield/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd7e600e3d341579","mcp_get_code":{"code_sha256":"dd7e600e3d341579"}},{"arxiv_id":"2502.06352","paper":"/paper/lantern-enhanced-relaxed-speculative-decoding","title":"LANTERN++: Enhancing Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models","date":"2025-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jadohu/lantern","path":"entrypoints/generate_images.py","file_url":"https://github.com/jadohu/lantern/blob/HEAD/entrypoints/generate_images.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f52f760cc0bc6497","mcp_get_code":{"code_sha256":"f52f760cc0bc6497"}},{"arxiv_id":"2502.02096","paper":"/paper/dual-flow-transferable-multi-target-instance","title":"Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization","date":"2025-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Chyxx/Dual-Flow","path":"utils.py","file_url":"https://github.com/Chyxx/Dual-Flow/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a4d8e6df8b1eaa8a","mcp_get_code":{"code_sha256":"a4d8e6df8b1eaa8a"}},{"arxiv_id":"2501.19374","paper":"/paper/fixing-the-double-penalty-in-data-driven","title":"Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function","date":"2025-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csubich/graphcast","path":"forecast/generate_model.py","file_url":"https://github.com/csubich/graphcast/blob/HEAD/forecast/generate_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"624011e6c460d5b0","mcp_get_code":{"code_sha256":"624011e6c460d5b0"}},{"arxiv_id":"2501.07575","paper":"/paper/dataset-distillation-via-committee-voting","title":"Dataset Distillation via Committee Voting","date":"2025-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiacheng8/cv-dd","path":"squeeze/squeeze_imagenet1k_prior.py","file_url":"https://github.com/jiacheng8/cv-dd/blob/HEAD/squeeze/squeeze_imagenet1k_prior.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c0c32e1872910304","mcp_get_code":{"code_sha256":"c0c32e1872910304"}},{"arxiv_id":"2501.07575","paper":"/paper/dataset-distillation-via-committee-voting","title":"Dataset Distillation via Committee Voting","date":"2025-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiacheng8/cv-dd","path":"squeeze/squeeze_imagenette.py","file_url":"https://github.com/jiacheng8/cv-dd/blob/HEAD/squeeze/squeeze_imagenette.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8c08fe6238212dd8","mcp_get_code":{"code_sha256":"8c08fe6238212dd8"}},{"arxiv_id":"2412.07724","paper":"/paper/granite-guardian","title":"Granite Guardian","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm-granite/granite-guardian","path":"evaluation/run_eval.py","file_url":"https://github.com/ibm-granite/granite-guardian/blob/HEAD/evaluation/run_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"87ecbe902110b5cb","mcp_get_code":{"code_sha256":"87ecbe902110b5cb"}},{"arxiv_id":"2412.07618","paper":"/paper/adapting-to-non-stationary-environments-multi","title":"Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"futureeeeee/dynamic-rag","path":"nn_router.py","file_url":"https://github.com/futureeeeee/dynamic-rag/blob/HEAD/nn_router.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ac17353eba9bc3d","mcp_get_code":{"code_sha256":"7ac17353eba9bc3d"}},{"arxiv_id":"2412.06660","paper":"/paper/mumu-llama-multi-modal-music-understanding","title":"MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"shansongliu/M2UGen","path":"DataSet/MUEdit/mistral.py","file_url":"https://github.com/shansongliu/M2UGen/blob/HEAD/DataSet/MUEdit/mistral.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08e8f3d4af99bdb4","mcp_get_code":{"code_sha256":"08e8f3d4af99bdb4"}},{"arxiv_id":"2412.06660","paper":"/paper/mumu-llama-multi-modal-music-understanding","title":"MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"shansongliu/M2UGen","path":"DataSet/MUImage/llava_caption.py","file_url":"https://github.com/shansongliu/M2UGen/blob/HEAD/DataSet/MUImage/llava_caption.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11e5e8810f15e38a","mcp_get_code":{"code_sha256":"11e5e8810f15e38a"}},{"arxiv_id":"2412.04445","paper":"/paper/moto-latent-motion-token-as-the-bridging","title":"Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videos","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencentarc/moto","path":"common/models/model_utils.py","file_url":"https://github.com/tencentarc/moto/blob/HEAD/common/models/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"84b1cfbe36d57648","mcp_get_code":{"code_sha256":"84b1cfbe36d57648"}},{"arxiv_id":"2411.19946","paper":"/paper/delt-a-simple-diversity-driven-earlylate","title":"DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation","date":"2024-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vila-lab/delt","path":"recover/models.py","file_url":"https://github.com/vila-lab/delt/blob/HEAD/recover/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"952e2a07853f7c8e","mcp_get_code":{"code_sha256":"952e2a07853f7c8e"}},{"arxiv_id":"2411.17116","paper":"/paper/star-attention-efficient-llm-inference-over","title":"Star Attention: Efficient LLM Inference over Long Sequences","date":"2024-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVIDIA/Star-Attention","path":"run_star_attn_inference.py","file_url":"https://github.com/NVIDIA/Star-Attention/blob/HEAD/run_star_attn_inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0ddc654f908b304f","mcp_get_code":{"code_sha256":"0ddc654f908b304f"}},{"arxiv_id":"2411.11407","paper":"/paper/the-dark-side-of-trust-authority-citation","title":"The Dark Side of Trust: Authority Citation-Driven Jailbreak Attacks on Large Language Models","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YancyKahn/DarkCite","path":"utils/language_models.py","file_url":"https://github.com/YancyKahn/DarkCite/blob/HEAD/utils/language_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"47f2fc3db88bc34a","mcp_get_code":{"code_sha256":"47f2fc3db88bc34a"}},{"arxiv_id":"2411.09688","paper":"/paper/squeezed-attention-accelerating-long-context","title":"Squeezed Attention: Accelerating Long Context Length LLM Inference","date":"2024-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SqueezeAILab/SqueezedAttention","path":"utils/model_parse.py","file_url":"https://github.com/SqueezeAILab/SqueezedAttention/blob/HEAD/utils/model_parse.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b66c43d90a1a3a1b","mcp_get_code":{"code_sha256":"b66c43d90a1a3a1b"}},{"arxiv_id":"2411.07404","paper":"/paper/controllable-context-sensitivity-and-the-knob","title":"Controllable Context Sensitivity and the Knob Behind It","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdu4108/context-vs-prior-finetuning","path":"analysis/circuit_utils/model.py","file_url":"https://github.com/kdu4108/context-vs-prior-finetuning/blob/HEAD/analysis/circuit_utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ffa9d912bb1dd857","mcp_get_code":{"code_sha256":"ffa9d912bb1dd857"}},{"arxiv_id":"2411.06424","paper":"/paper/ablation-is-not-enough-to-emulate-dpo-how","title":"Beyond Toxic Neurons: A Mechanistic Analysis of DPO for Toxicity Reduction","date":"2024-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yushi-y/dpo-toxic-neurons","path":"evaluation/eval_utils.py","file_url":"https://github.com/yushi-y/dpo-toxic-neurons/blob/HEAD/evaluation/eval_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89200b27db47f6ea","mcp_get_code":{"code_sha256":"89200b27db47f6ea"}},{"arxiv_id":"2411.04165","paper":"/paper/bio-xlstm-generative-modeling-representation","title":"Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/chem-xlstm","path":"chemxlstm/evaluate_cond_gen.py","file_url":"https://github.com/ml-jku/chem-xlstm/blob/HEAD/chemxlstm/evaluate_cond_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"df4c6239424d356d","mcp_get_code":{"code_sha256":"df4c6239424d356d"}},{"arxiv_id":"2411.03554","paper":"/paper/benchmarking-vision-language-model-unlearning","title":"Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"safolab-wisc/fiubench","path":"eval/eval_mme.py","file_url":"https://github.com/safolab-wisc/fiubench/blob/HEAD/eval/eval_mme.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"05febc93f9059af6","mcp_get_code":{"code_sha256":"05febc93f9059af6"}},{"arxiv_id":"2411.02537","paper":"/paper/inquire-a-natural-world-text-to-image","title":"INQUIRE: A Natural World Text-to-Image Retrieval Benchmark","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biubug6/Pytorch_Retinaface","path":"convert_to_onnx.py","file_url":"https://github.com/biubug6/Pytorch_Retinaface/blob/HEAD/convert_to_onnx.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec9dcc3bf7178b3c","mcp_get_code":{"code_sha256":"ec9dcc3bf7178b3c"}},{"arxiv_id":"2411.00300","paper":"/paper/rationale-guided-retrieval-augmented","title":"Rationale-Guided Retrieval Augmented Generation for Medical Question Answering","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmis-lab/rag2","path":"classifier/utils.py","file_url":"https://github.com/dmis-lab/rag2/blob/HEAD/classifier/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f013b9029a24e889","mcp_get_code":{"code_sha256":"f013b9029a24e889"}},{"arxiv_id":"2410.20886","paper":"/paper/codes-benchmarking-coupled-ode-surrogates","title":"CODES: Benchmarking Coupled ODE Surrogates","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robin-janssen/codes-benchmark","path":"codes/benchmark/bench_utils.py","file_url":"https://github.com/robin-janssen/codes-benchmark/blob/HEAD/codes/benchmark/bench_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"d7822d4415369a7a","mcp_get_code":{"code_sha256":"d7822d4415369a7a"}},{"arxiv_id":"2410.15153","paper":"/paper/evaluating-deep-unlearning-in-large-language","title":"Evaluating Deep Unlearning in Large Language Models","date":"2024-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wrh14/deep_unlearning","path":"task_vector.py","file_url":"https://github.com/wrh14/deep_unlearning/blob/HEAD/task_vector.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5049c9b469cf9","mcp_get_code":{"code_sha256":"fac5049c9b469cf9"}},{"arxiv_id":"2410.14273","paper":"/paper/reef-representation-encoding-fingerprints-for","title":"REEF: Representation Encoding Fingerprints for Large Language Models","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmylla/reef","path":"src/generate_activations.py","file_url":"https://github.com/tmylla/reef/blob/HEAD/src/generate_activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9058a74d60bf8126","mcp_get_code":{"code_sha256":"9058a74d60bf8126"}},{"arxiv_id":"2410.12409","paper":"/paper/revealing-the-barriers-of-language-agents-in","title":"Revealing the Barriers of Language Agents in Planning","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hsaest/Agent-Planning-Analysis","path":"code/AttrScoreCalc/blocksWorld.py","file_url":"https://github.com/hsaest/Agent-Planning-Analysis/blob/HEAD/code/AttrScoreCalc/blocksWorld.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a00667d66f69cefc","mcp_get_code":{"code_sha256":"a00667d66f69cefc"}},{"arxiv_id":"2410.12409","paper":"/paper/revealing-the-barriers-of-language-agents-in","title":"Revealing the Barriers of Language Agents in Planning","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hsaest/Agent-Planning-Analysis","path":"code/AttrScoreCalc/travelPlanner.py","file_url":"https://github.com/hsaest/Agent-Planning-Analysis/blob/HEAD/code/AttrScoreCalc/travelPlanner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f637f9aa877b3186","mcp_get_code":{"code_sha256":"f637f9aa877b3186"}},{"arxiv_id":"2410.10343","paper":"/paper/locking-down-the-finetuned-llms-safety","title":"Locking Down the Finetuned LLMs Safety","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhu-minjun/safetylock","path":"eval/eval_utils/model_utils.py","file_url":"https://github.com/zhu-minjun/safetylock/blob/HEAD/eval/eval_utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"efea256714a4cb99","mcp_get_code":{"code_sha256":"efea256714a4cb99"}},{"arxiv_id":"2410.10139","paper":"/paper/mmie-massive-multimodal-interleaved","title":"MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lillianwei-h/MMIE","path":"load_model.py","file_url":"https://github.com/Lillianwei-h/MMIE/blob/HEAD/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62678e1b98d625c9","mcp_get_code":{"code_sha256":"62678e1b98d625c9"}},{"arxiv_id":"2410.06262","paper":"/paper/symdiff-equivariant-diffusion-via-stochastic","title":"SymDiff: Equivariant Diffusion via Stochastic Symmetrisation","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leozhangML/SymDiff","path":"utils.py","file_url":"https://github.com/leozhangML/SymDiff/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc4da23dd75dcb46","mcp_get_code":{"code_sha256":"fc4da23dd75dcb46"}},{"arxiv_id":"2410.03658","paper":"/paper/raft-realistic-attacks-to-fool-text-detectors","title":"RAFT: Realistic Attacks to Fool Text Detectors","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jameslwang/raft","path":"detectors/utils/model.py","file_url":"https://github.com/jameslwang/raft/blob/HEAD/detectors/utils/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"686f5f3ade5903a7","mcp_get_code":{"code_sha256":"686f5f3ade5903a7"}},{"arxiv_id":"2410.03658","paper":"/paper/raft-realistic-attacks-to-fool-text-detectors","title":"RAFT: Realistic Attacks to Fool Text Detectors","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jameslwang/raft","path":"detectors/model.py","file_url":"https://github.com/jameslwang/raft/blob/HEAD/detectors/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"37fee88bd805883d","mcp_get_code":{"code_sha256":"37fee88bd805883d"}},{"arxiv_id":"2410.02184","paper":"/paper/codejudge-evaluating-code-generation-with","title":"CodeJudge: Evaluating Code Generation with Large Language Models","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VichyTong/CodeJudge","path":"code_model_score/utils.py","file_url":"https://github.com/VichyTong/CodeJudge/blob/HEAD/code_model_score/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"441183fe72976e33","mcp_get_code":{"code_sha256":"441183fe72976e33"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/blip2.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/blip2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"56a36c6289b05241","mcp_get_code":{"code_sha256":"56a36c6289b05241"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/deepseek.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/deepseek.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4396be49952addde","mcp_get_code":{"code_sha256":"4396be49952addde"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/instructblip.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/instructblip.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fae4461a850afc1","mcp_get_code":{"code_sha256":"2fae4461a850afc1"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/llama.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/llama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d37a3a4453bba54d","mcp_get_code":{"code_sha256":"d37a3a4453bba54d"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/llava.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/llava.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12a456d721d6bf85","mcp_get_code":{"code_sha256":"12a456d721d6bf85"}},{"arxiv_id":"2409.15477","paper":"/paper/mediconfusion-can-you-trust-your-ai","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIF4S/MediConfusion","path":"Models/molmo.py","file_url":"https://github.com/AIF4S/MediConfusion/blob/HEAD/Models/molmo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a14ab87f7b097f4b","mcp_get_code":{"code_sha256":"a14ab87f7b097f4b"}},{"arxiv_id":"2409.15180","paper":"/paper/a-comprehensive-survey-with-critical-analysis","title":"A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"tamlhp/dfd_benchmark","path":"detect_img.py","file_url":"https://github.com/tamlhp/dfd_benchmark/blob/HEAD/detect_img.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc30c4178236c621","mcp_get_code":{"code_sha256":"dc30c4178236c621"}},{"arxiv_id":"2409.14713","paper":"/paper/phantom-of-latent-for-large-language-and","title":"Phantom of Latent for Large Language and Vision Models","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"byungkwanlee/phantom","path":"model/load_model.py","file_url":"https://github.com/byungkwanlee/phantom/blob/HEAD/model/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db11a804a6adefad","mcp_get_code":{"code_sha256":"db11a804a6adefad"}},{"arxiv_id":"2409.09369","paper":"/paper/interpretable-vision-language-survival","title":"Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology","date":"2024-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liupei101/VLSA","path":"model/utils.py","file_url":"https://github.com/liupei101/VLSA/blob/HEAD/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1e0e3df7d917f5c1","mcp_get_code":{"code_sha256":"1e0e3df7d917f5c1"}},{"arxiv_id":"2409.06851","paper":"/paper/lime-m-less-is-more-for-evaluation-of-mllms","title":"LIME: Less Is More for MLLM Evaluation","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangreen0210/lime","path":"data_curation_pipeline/gpt.py","file_url":"https://github.com/kangreen0210/lime/blob/HEAD/data_curation_pipeline/gpt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e0b2efb35ad35f46","mcp_get_code":{"code_sha256":"e0b2efb35ad35f46"}},{"arxiv_id":"2409.05122","paper":"/paper/pmt-progressive-mean-teacher-via-exploring","title":"PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation","date":"2024-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"axi404/pmt","path":"code/utils/util.py","file_url":"https://github.com/axi404/pmt/blob/HEAD/code/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"2409.02606","paper":null,"title":"arXiv:2409.02606","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":null,"repo":"princetonlips/neural_fdm","path":"src/neural_fdm/serialization.py","file_url":"https://github.com/princetonlips/neural_fdm/blob/HEAD/src/neural_fdm/serialization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8cb8ddde6acd5011","mcp_get_code":{"code_sha256":"8cb8ddde6acd5011"}},{"arxiv_id":"2408.16218","paper":"/paper/targeted-cause-discovery-with-data-driven","title":"Large-Scale Targeted Cause Discovery with Data-Driven Learning","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/targeted-cause-discovery","path":"model/load.py","file_url":"https://github.com/snu-mllab/targeted-cause-discovery/blob/HEAD/model/load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db44474a62cf3d03","mcp_get_code":{"code_sha256":"db44474a62cf3d03"}},{"arxiv_id":"2408.13442","paper":"/paper/a-law-of-next-token-prediction-in-large","title":"A Law of Next-Token Prediction in Large Language Models","date":"2024-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hornhehhf/llm-ell","path":"feature_learning.py","file_url":"https://github.com/hornhehhf/llm-ell/blob/HEAD/feature_learning.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fe7cbd51bab2b6f","mcp_get_code":{"code_sha256":"8fe7cbd51bab2b6f"}},{"arxiv_id":"2408.11085","paper":"/paper/gsloc-efficient-camera-pose-refinement-via-3d","title":"GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting","date":"2024-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XRIM-Lab/GS-CPR","path":"mast3r/model.py","file_url":"https://github.com/XRIM-Lab/GS-CPR/blob/HEAD/mast3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e3f140b86bb9a08","mcp_get_code":{"code_sha256":"1e3f140b86bb9a08"}},{"arxiv_id":"2408.10441","paper":"/paper/goldfish-monolingual-language-models-for-350","title":"Goldfish: Monolingual Language Models for 350 Languages","date":"2024-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tylerachang/goldfish","path":"eval_code/belebele_eval_goldfish.py","file_url":"https://github.com/tylerachang/goldfish/blob/HEAD/eval_code/belebele_eval_goldfish.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c362562812238de9","mcp_get_code":{"code_sha256":"c362562812238de9"}},{"arxiv_id":"2408.09121","paper":"/paper/selective-prompt-anchoring-for-code","title":"Selective Prompt Anchoring for Code Generation","date":"2024-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"magic-YuanTian/Selective-Prompt-Anchoring","path":"obsolete/obsolete/weighted_utils/weighted_text_utils.py","file_url":"https://github.com/magic-YuanTian/Selective-Prompt-Anchoring/blob/HEAD/obsolete/obsolete/weighted_utils/weighted_text_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f32275a7d9ecf730","mcp_get_code":{"code_sha256":"f32275a7d9ecf730"}},{"arxiv_id":"2408.08661","paper":"/paper/mia-tuner-adapting-large-language-models-as","title":"MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector","date":"2024-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjfu99/mia-tuner","path":"run_baselines.py","file_url":"https://github.com/wjfu99/mia-tuner/blob/HEAD/run_baselines.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c4f49de1c200db3c","mcp_get_code":{"code_sha256":"c4f49de1c200db3c"}},{"arxiv_id":"2408.07888","paper":"/paper/fine-tuning-large-language-models-with-human","title":"Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Oxford-AI-for-Society/human-learning-strategies","path":"training/fine_tuning/shared_utils.py","file_url":"https://github.com/Oxford-AI-for-Society/human-learning-strategies/blob/HEAD/training/fine_tuning/shared_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"912f3b9102f82700","mcp_get_code":{"code_sha256":"912f3b9102f82700"}},{"arxiv_id":"2408.06223","paper":"/paper/on-effects-of-steering-latent-representation","title":"On Effects of Steering Latent Representation for Large Language Model Unlearning","date":"2024-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RebelsNLU-jaist/llm-unlearning","path":"baselines/adap_rmu/utils.py","file_url":"https://github.com/RebelsNLU-jaist/llm-unlearning/blob/HEAD/baselines/adap_rmu/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7bdf184a0ba57b39","mcp_get_code":{"code_sha256":"7bdf184a0ba57b39"}},{"arxiv_id":"2408.04810","paper":"/paper/unibench-visual-reasoning-requires-rethinking","title":"UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling","date":"2024-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/unibench","path":"unibench/models_zoo/registry.py","file_url":"https://github.com/facebookresearch/unibench/blob/HEAD/unibench/models_zoo/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b81a08983be5a4ee","mcp_get_code":{"code_sha256":"b81a08983be5a4ee"}},{"arxiv_id":"2408.04662","paper":"/paper/citekit-a-modular-toolkit-for-large-language","title":"Citekit: A Modular Toolkit for Large Language Model Citation Generation","date":"2024-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjj1017/citekit","path":"citekit/cite_modules/LLM.py","file_url":"https://github.com/sjj1017/citekit/blob/HEAD/citekit/cite_modules/LLM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b1aa6c33e7b0d55","mcp_get_code":{"code_sha256":"2b1aa6c33e7b0d55"}},{"arxiv_id":"2408.04284","paper":"/paper/llm-detectaive-a-tool-for-fine-grained","title":"LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbzuai-nlp/llm-detectaive","path":"script/llm-detectaive.py","file_url":"https://github.com/mbzuai-nlp/llm-detectaive/blob/HEAD/script/llm-detectaive.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4babb7ab0240f517","mcp_get_code":{"code_sha256":"4babb7ab0240f517"}},{"arxiv_id":"2407.17023","paper":"/paper/from-internal-conflict-to-contextual","title":"DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models","date":"2024-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"copenlu/dynamicqa","path":"final_code/generation_utils.py","file_url":"https://github.com/copenlu/dynamicqa/blob/HEAD/final_code/generation_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60b894ff063b237b","mcp_get_code":{"code_sha256":"60b894ff063b237b"}},{"arxiv_id":"2407.15847","paper":"/paper/llmmap-fingerprinting-for-large-language","title":"LLMmap: Fingerprinting For Large Language Models","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pasquini-dario/LLMmap","path":"LLMmap/embedding_model.py","file_url":"https://github.com/pasquini-dario/LLMmap/blob/HEAD/LLMmap/embedding_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9ee90e0c985481bb","mcp_get_code":{"code_sha256":"9ee90e0c985481bb"}},{"arxiv_id":"2407.14126","paper":"/paper/mono-vifi-a-unified-learning-framework-for","title":"Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujf1226/mono-vifi","path":"evaluate_depth.py","file_url":"https://github.com/liujf1226/mono-vifi/blob/HEAD/evaluate_depth.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4718c67d38954a25","mcp_get_code":{"code_sha256":"4718c67d38954a25"}},{"arxiv_id":"2407.14126","paper":"/paper/mono-vifi-a-unified-learning-framework-for","title":"Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujf1226/mono-vifi","path":"evaluate_depth_mf.py","file_url":"https://github.com/liujf1226/mono-vifi/blob/HEAD/evaluate_depth_mf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4818f3b037653060","mcp_get_code":{"code_sha256":"4818f3b037653060"}},{"arxiv_id":"2407.13803","paper":"/paper/less-is-more-sparse-watermarking-in-llms-with","title":"Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mail-research/sparse-llm-watermarking","path":"watermark/run_generate.py","file_url":"https://github.com/mail-research/sparse-llm-watermarking/blob/HEAD/watermark/run_generate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c23be2f93d77eb72","mcp_get_code":{"code_sha256":"c23be2f93d77eb72"}},{"arxiv_id":"2407.11282","paper":"/paper/uncertainty-is-fragile-manipulating","title":"Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qcznlp/uncertainty","path":"get_probability_distribution.py","file_url":"https://github.com/qcznlp/uncertainty/blob/HEAD/get_probability_distribution.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7a3c0ed788f49496","mcp_get_code":{"code_sha256":"7a3c0ed788f49496"}},{"arxiv_id":"2407.08473","paper":"/paper/natural-language-is-not-enough-benchmarking","title":"Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aichipdesign/chipgptv","path":"test_benchmark/rtlcoder_finetune_benchmark.py","file_url":"https://github.com/aichipdesign/chipgptv/blob/HEAD/test_benchmark/rtlcoder_finetune_benchmark.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ec27caa35181009","mcp_get_code":{"code_sha256":"5ec27caa35181009"}},{"arxiv_id":"2407.02099","paper":"/paper/helpful-assistant-or-fruitful-facilitator","title":"Helpful assistant or fruitful facilitator? Investigating how personas affect language model behavior","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peluz/persona-behavior","path":"models/inference.py","file_url":"https://github.com/peluz/persona-behavior/blob/HEAD/models/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"789a9a0046f0f6b5","mcp_get_code":{"code_sha256":"789a9a0046f0f6b5"}},{"arxiv_id":"2407.01523","paper":"/paper/mmlongbench-doc-benchmarking-long-context","title":"MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mayubo2333/mmlongbench-doc","path":"run_lvlm.py","file_url":"https://github.com/mayubo2333/mmlongbench-doc/blob/HEAD/run_lvlm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3c76aa11708f6da6","mcp_get_code":{"code_sha256":"3c76aa11708f6da6"}},{"arxiv_id":"2407.07723","paper":"/paper/understanding-is-compression","title":"Lossless data compression by large models","date":"2024-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcGill-NLP/medal","path":"utils.py","file_url":"https://github.com/mcGill-NLP/medal/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c2a1f556b1e44d6","mcp_get_code":{"code_sha256":"0c2a1f556b1e44d6"}},{"arxiv_id":"2406.18925","paper":"/paper/selective-vision-is-the-challenge-for-visual","title":"Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiwanChung/VisArgs","path":"src/visarg/tasks/classification/classification.py","file_url":"https://github.com/JiwanChung/VisArgs/blob/HEAD/src/visarg/tasks/classification/classification.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe5e851bbfb6cb39","mcp_get_code":{"code_sha256":"fe5e851bbfb6cb39"}},{"arxiv_id":"2406.18925","paper":"/paper/selective-vision-is-the-challenge-for-visual","title":"Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiwanchung/visargs","path":"src/visarg/tasks/localization/openset.py","file_url":"https://github.com/jiwanchung/visargs/blob/HEAD/src/visarg/tasks/localization/openset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"825db40de691eea5","mcp_get_code":{"code_sha256":"825db40de691eea5"}},{"arxiv_id":"2406.18925","paper":"/paper/selective-vision-is-the-challenge-for-visual","title":"Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiwanChung/VisArgs","path":"src/visarg/tasks/generation/generation.py","file_url":"https://github.com/JiwanChung/VisArgs/blob/HEAD/src/visarg/tasks/generation/generation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0f9943080d349652","mcp_get_code":{"code_sha256":"0f9943080d349652"}},{"arxiv_id":"2406.17746","paper":"/paper/recite-reconstruct-recollect-memorization-in","title":"Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eleutherai/semantic-memorization","path":"inference.py","file_url":"https://github.com/eleutherai/semantic-memorization/blob/HEAD/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6da393d365fbc8a","mcp_get_code":{"code_sha256":"e6da393d365fbc8a"}},{"arxiv_id":"2406.15334","paper":"/paper/multimodal-task-vectors-enable-many-shot","title":"Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Brandon3964/MultiModal-Task-Vector","path":"MTV/mtv_utils.py","file_url":"https://github.com/Brandon3964/MultiModal-Task-Vector/blob/HEAD/MTV/mtv_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2534a18f2f56fd6c","mcp_get_code":{"code_sha256":"2534a18f2f56fd6c"}},{"arxiv_id":"2406.14194","paper":"/paper/vlbiasbench-a-comprehensive-benchmark-for","title":"VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangkui-cao/vlbiasbench","path":"evaluation/models/load_model.py","file_url":"https://github.com/xiangkui-cao/vlbiasbench/blob/HEAD/evaluation/models/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ea1db8fc1ef47f9e","mcp_get_code":{"code_sha256":"ea1db8fc1ef47f9e"}},{"arxiv_id":"2406.12775","paper":"/paper/hopping-too-late-exploring-the-limitations-of","title":"Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edenbiran/HoppingTooLate","path":"src/utils.py","file_url":"https://github.com/edenbiran/HoppingTooLate/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d02aac80c0eae1a","mcp_get_code":{"code_sha256":"9d02aac80c0eae1a"}},{"arxiv_id":"2406.12329","paper":"/paper/snap-unlearning-selective-knowledge-in-large","title":"Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brightjade/Opt-Out","path":"model.py","file_url":"https://github.com/brightjade/Opt-Out/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1e894760d60a6582","mcp_get_code":{"code_sha256":"1e894760d60a6582"}},{"arxiv_id":"2406.09756","paper":"/paper/grounding-image-matching-in-3d-with-mast3r","title":"Grounding Image Matching in 3D with MASt3R","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver/mast3r","path":"mast3r/model.py","file_url":"https://github.com/naver/mast3r/blob/HEAD/mast3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9a09f31d98cc4d88","mcp_get_code":{"code_sha256":"9a09f31d98cc4d88"}},{"arxiv_id":"2406.08993","paper":"/paper/classic-gnns-are-strong-baselines-reassessing","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LUOyk1999/tunedGNN","path":"large_graph/logger.py","file_url":"https://github.com/LUOyk1999/tunedGNN/blob/HEAD/large_graph/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54fd4512b8397f45","mcp_get_code":{"code_sha256":"54fd4512b8397f45"}},{"arxiv_id":"2406.04823","paper":"/paper/berts-are-generative-in-context-learners","title":"BERTs are Generative In-Context Learners","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltgoslo/bert-in-context","path":"language-modeling/hellaswag.py","file_url":"https://github.com/ltgoslo/bert-in-context/blob/HEAD/language-modeling/hellaswag.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"081ead69959a12d1","mcp_get_code":{"code_sha256":"081ead69959a12d1"}},{"arxiv_id":"2406.04823","paper":"/paper/berts-are-generative-in-context-learners","title":"BERTs are Generative In-Context Learners","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltgoslo/bert-in-context","path":"needle-in-a-haystack/haystack.py","file_url":"https://github.com/ltgoslo/bert-in-context/blob/HEAD/needle-in-a-haystack/haystack.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3585e698dbdb0fc1","mcp_get_code":{"code_sha256":"3585e698dbdb0fc1"}},{"arxiv_id":"2406.04823","paper":"/paper/berts-are-generative-in-context-learners","title":"BERTs are Generative In-Context Learners","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltgoslo/bert-in-context","path":"question-answering/natural_questions.py","file_url":"https://github.com/ltgoslo/bert-in-context/blob/HEAD/question-answering/natural_questions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"03857b5d2df81f28","mcp_get_code":{"code_sha256":"03857b5d2df81f28"}},{"arxiv_id":"2406.04673","paper":"/paper/melfusion-synthesizing-music-from-image-and","title":"MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"schowdhury671/melfusion","path":"models.py","file_url":"https://github.com/schowdhury671/melfusion/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb989094ba0ffe5f","mcp_get_code":{"code_sha256":"bb989094ba0ffe5f"}},{"arxiv_id":"2406.01589","paper":"/paper/tilting-the-odds-at-the-lottery-the-interplay","title":"Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qglht/repal","path":"main/model.py","file_url":"https://github.com/qglht/repal/blob/HEAD/main/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c71208dc95adea72","mcp_get_code":{"code_sha256":"c71208dc95adea72"}},{"arxiv_id":"2406.00799","paper":"/paper/are-you-still-on-track-catching-llm-task","title":"Get my drift? Catching LLM Task Drift with Activation Deltas","date":"2024-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/TaskTracker","path":"task_tracker/utils/model.py","file_url":"https://github.com/microsoft/TaskTracker/blob/HEAD/task_tracker/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d64b715a817507d","mcp_get_code":{"code_sha256":"1d64b715a817507d"}},{"arxiv_id":"2405.18392","paper":"/paper/scaling-laws-and-compute-optimal-training","title":"Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frotaur/icmlbackperp","path":"modules/models/load_model.py","file_url":"https://github.com/frotaur/icmlbackperp/blob/HEAD/modules/models/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"98497683abdc8f17","mcp_get_code":{"code_sha256":"98497683abdc8f17"}},{"arxiv_id":"2405.17374","paper":"/paper/navigating-the-safety-landscape-measuring","title":"Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShengYun-Peng/llm-landscape","path":"src/llm/inference.py","file_url":"https://github.com/ShengYun-Peng/llm-landscape/blob/HEAD/src/llm/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"054bb0c2b5d9de0c","mcp_get_code":{"code_sha256":"054bb0c2b5d9de0c"}},{"arxiv_id":"2405.16681","paper":"/paper/triple-preference-optimization-achieving","title":"Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sahsaeedi/triple-preference-optimization","path":"utils/model_utils.py","file_url":"https://github.com/sahsaeedi/triple-preference-optimization/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f08eeb1ca2446b4e","mcp_get_code":{"code_sha256":"f08eeb1ca2446b4e"}},{"arxiv_id":"2405.11930","paper":"/paper/data-contamination-calibration-for-black-box","title":"Data Contamination Calibration for Black-box LLMs","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yyy01/pac","path":"src/prob.py","file_url":"https://github.com/yyy01/pac/blob/HEAD/src/prob.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c19981ce5643620","mcp_get_code":{"code_sha256":"7c19981ce5643620"}},{"arxiv_id":"2405.08553","paper":"/paper/improving-transformers-with-dynamically","title":"Improving Transformers with Dynamically Composable Multi-Head Attention","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caiyun-ai/dcformer","path":"pytorch/dcformer/maxtext2torch.py","file_url":"https://github.com/caiyun-ai/dcformer/blob/HEAD/pytorch/dcformer/maxtext2torch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f313d5feb241ec85","mcp_get_code":{"code_sha256":"f313d5feb241ec85"}},{"arxiv_id":"2405.07938","paper":"/paper/econlogicqa-a-question-answering-benchmark","title":"EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning","date":"2024-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yinzhu-quan/lm-evaluation-harness","path":"lm_eval/models/nemo_lm.py","file_url":"https://github.com/yinzhu-quan/lm-evaluation-harness/blob/HEAD/lm_eval/models/nemo_lm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ff6d4e326d3c0db","mcp_get_code":{"code_sha256":"4ff6d4e326d3c0db"}},{"arxiv_id":"2405.06708","paper":"/paper/langcell-language-cell-pre-training-for-cell","title":"LangCell: Language-Cell Pre-training for Cell Identity Understanding","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PharMolix/LangCell","path":"geneformer_001/geneformer/in_silico_perturber.py","file_url":"https://github.com/PharMolix/LangCell/blob/HEAD/geneformer_001/geneformer/in_silico_perturber.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fca4bb791a0b4c8f","mcp_get_code":{"code_sha256":"fca4bb791a0b4c8f"}},{"arxiv_id":"2405.04405","paper":"/paper/weakly-supervised-residual-evidential","title":"Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation","date":"2024-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liupei101/MIREL","path":"model/model_utils.py","file_url":"https://github.com/liupei101/MIREL/blob/HEAD/model/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6640364ac6052908","mcp_get_code":{"code_sha256":"6640364ac6052908"}},{"arxiv_id":"2405.03000","paper":"/paper/medadapter-efficient-test-time-adaptation-of","title":"MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning","date":"2024-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wshi83/medadapter","path":"generator/vanilla_trainer.py","file_url":"https://github.com/wshi83/medadapter/blob/HEAD/generator/vanilla_trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3ceed634ac6ca1d2","mcp_get_code":{"code_sha256":"3ceed634ac6ca1d2"}},{"arxiv_id":"2405.00722","paper":"/paper/llms-for-generating-and-evaluating","title":"LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study","date":"2024-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aix-group/llms-for-cfs","path":"src/utils/utils_gen.py","file_url":"https://github.com/aix-group/llms-for-cfs/blob/HEAD/src/utils/utils_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f09920e251b9c0e9","mcp_get_code":{"code_sha256":"f09920e251b9c0e9"}},{"arxiv_id":"2404.18824","paper":"/paper/benchmarking-benchmark-leakage-in-large","title":"Benchmarking Benchmark Leakage in Large Language Models","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gair-nlp/benbench","path":"src/ppl_and_ngram_utils.py","file_url":"https://github.com/gair-nlp/benbench/blob/HEAD/src/ppl_and_ngram_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"76762245877c5d28","mcp_get_code":{"code_sha256":"76762245877c5d28"}},{"arxiv_id":"2404.13207","paper":"/paper/stark-benchmarking-llm-retrieval-on-textual","title":"STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-stanford/stark","path":"stark_qa/load_model.py","file_url":"https://github.com/snap-stanford/stark/blob/HEAD/stark_qa/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d65a8f1d30d841f","mcp_get_code":{"code_sha256":"4d65a8f1d30d841f"}},{"arxiv_id":"2404.13013","paper":"/paper/groma-localized-visual-tokenization-for","title":"Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FoundationVision/Groma","path":"groma/serve/model_worker.py","file_url":"https://github.com/FoundationVision/Groma/blob/HEAD/groma/serve/model_worker.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f78ec6fd3d821f61","mcp_get_code":{"code_sha256":"f78ec6fd3d821f61"}},{"arxiv_id":"2404.11825","paper":"/paper/hypergraph-self-supervised-learning-with","title":"Hypergraph Self-supervised Learning with Sampling-efficient Signals","date":"2024-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Coco-Hut/SE-HSSL","path":"SE-HSSL/utils.py","file_url":"https://github.com/Coco-Hut/SE-HSSL/blob/HEAD/SE-HSSL/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e805e4e599834860","mcp_get_code":{"code_sha256":"e805e4e599834860"}},{"arxiv_id":"2404.11262","paper":"/paper/sampling-based-pseudo-likelihood-for","title":"Sampling-based Pseudo-Likelihood for Membership Inference Attacks","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nlp-titech/samia","path":"src/model_loader.py","file_url":"https://github.com/nlp-titech/samia/blob/HEAD/src/model_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5572f146d01337d4","mcp_get_code":{"code_sha256":"5572f146d01337d4"}},{"arxiv_id":"2404.06921","paper":"/paper/goex-perspectives-and-designs-towards-a","title":"GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShishirPatil/gorilla","path":"gorilla/inference/gorilla_eval.py","file_url":"https://github.com/ShishirPatil/gorilla/blob/HEAD/gorilla/inference/gorilla_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ba873c8d7fc8d9c3","mcp_get_code":{"code_sha256":"ba873c8d7fc8d9c3"}},{"arxiv_id":"2404.06003","paper":"/paper/freeeval-a-modular-framework-for-trustworthy","title":"FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wisdomshell/freeeval","path":"freeeval/models/local_hf_model.py","file_url":"https://github.com/wisdomshell/freeeval/blob/HEAD/freeeval/models/local_hf_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36e7c84d050efc4f","mcp_get_code":{"code_sha256":"36e7c84d050efc4f"}},{"arxiv_id":"2404.05225","paper":"/paper/layoutllm-layout-instruction-tuning-with","title":"LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlibabaResearch/AdvancedLiterateMachinery","path":"DocumentUnderstanding/GeoLayoutLM/model/geolayoutlm_vie.py","file_url":"https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/HEAD/DocumentUnderstanding/GeoLayoutLM/model/geolayoutlm_vie.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"60e8fd15d1cc8e40","mcp_get_code":{"code_sha256":"60e8fd15d1cc8e40"}},{"arxiv_id":"2404.01099","paper":"/paper/what-s-in-your-safe-data-identifying-benign","title":"What is in Your Safe Data? Identifying Benign Data that Breaks Safety","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/benign-data-breaks-safety","path":"safety_evaluation/eval_utils/model_utils.py","file_url":"https://github.com/princeton-nlp/benign-data-breaks-safety/blob/HEAD/safety_evaluation/eval_utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"efea256714a4cb99","mcp_get_code":{"code_sha256":"efea256714a4cb99"}},{"arxiv_id":"2404.01054","paper":"/paper/regularized-best-of-n-sampling-to-mitigate","title":"Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CyberAgentAILab/regularized-bon","path":"mbr/utils.py","file_url":"https://github.com/CyberAgentAILab/regularized-bon/blob/HEAD/mbr/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a28c6520702b745","mcp_get_code":{"code_sha256":"6a28c6520702b745"}},{"arxiv_id":"2403.20309","paper":"/paper/instantsplat-unbounded-sparse-view-pose-free","title":"InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/InstantSplat","path":"dust3r/model.py","file_url":"https://github.com/NVlabs/InstantSplat/blob/HEAD/dust3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e3f140b86bb9a08","mcp_get_code":{"code_sha256":"1e3f140b86bb9a08"}},{"arxiv_id":"2403.18775","paper":"/paper/imagenet-d-benchmarking-neural-network","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenshuang-zhang/imagenet_d","path":"utils/models.py","file_url":"https://github.com/chenshuang-zhang/imagenet_d/blob/HEAD/utils/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da517960652aeae9","mcp_get_code":{"code_sha256":"da517960652aeae9"}},{"arxiv_id":"2403.17983","paper":"/paper/is-watermarking-llm-generated-code-robust","title":"Is The Watermarking Of LLM-Generated Code Robust?","date":"2024-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uiuc-arc/llm-code-watermark","path":"lpw/lmw/demo_watermark.py","file_url":"https://github.com/uiuc-arc/llm-code-watermark/blob/HEAD/lpw/lmw/demo_watermark.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b63882b68514b2bc","mcp_get_code":{"code_sha256":"b63882b68514b2bc"}},{"arxiv_id":"2403.14403","paper":"/paper/adaptive-rag-learning-to-adapt-retrieval","title":"Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starsuzi/Adaptive-RAG","path":"classifier/utils.py","file_url":"https://github.com/starsuzi/Adaptive-RAG/blob/HEAD/classifier/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f013b9029a24e889","mcp_get_code":{"code_sha256":"f013b9029a24e889"}},{"arxiv_id":"2403.14312","paper":"/paper/chainlm-empowering-large-language-models-with","title":"ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rucaibox/chainlm","path":"debate/debate4cot.py","file_url":"https://github.com/rucaibox/chainlm/blob/HEAD/debate/debate4cot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5872a9172aa4cf7b","mcp_get_code":{"code_sha256":"5872a9172aa4cf7b"}},{"arxiv_id":"2403.08293","paper":"/paper/generative-pretrained-structured-transformers","title":"Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/structuredlm_rtdt","path":"model/generative_r2d2.py","file_url":"https://github.com/ant-research/structuredlm_rtdt/blob/HEAD/model/generative_r2d2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"68f9327845ca9499","mcp_get_code":{"code_sha256":"68f9327845ca9499"}},{"arxiv_id":"2403.08262","paper":"/paper/bitt-bi-directional-texture-reconstruction-of","title":"BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunminjin2/bitt","path":"models/model.py","file_url":"https://github.com/yunminjin2/bitt/blob/HEAD/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"3159379050a9be48","mcp_get_code":{"code_sha256":"3159379050a9be48"}},{"arxiv_id":"2403.06233","paper":"/paper/finding-visual-saliency-in-continuous-spike","title":"Finding Visual Saliency in Continuous Spike Stream","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BIT-Vision/SVS","path":"models/load_model.py","file_url":"https://github.com/BIT-Vision/SVS/blob/HEAD/models/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a56c89e70402fe2f","mcp_get_code":{"code_sha256":"a56c89e70402fe2f"}},{"arxiv_id":"2403.05010","paper":"/paper/rfwave-multi-band-rectified-flow-for-audio","title":"RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bfs18/rfwave","path":"inference_voc.py","file_url":"https://github.com/bfs18/rfwave/blob/HEAD/inference_voc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1e1caae2e9e424c","mcp_get_code":{"code_sha256":"c1e1caae2e9e424c"}},{"arxiv_id":"2403.04746","paper":"/paper/llms-in-the-imaginarium-tool-learning-through","title":"LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/simulated-trial-and-error","path":"llama-recipes/inference/model_utils.py","file_url":"https://github.com/microsoft/simulated-trial-and-error/blob/HEAD/llama-recipes/inference/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86440eb9f495e01b","mcp_get_code":{"code_sha256":"86440eb9f495e01b"}},{"arxiv_id":"2403.04706","paper":"/paper/common-7b-language-models-already-possess","title":"Common 7B Language Models Already Possess Strong Math Capabilities","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jerrywu-code/susgen","path":"src/template.py","file_url":"https://github.com/jerrywu-code/susgen/blob/HEAD/src/template.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c87d1b4488a54c82","mcp_get_code":{"code_sha256":"c87d1b4488a54c82"}},{"arxiv_id":"2403.04599","paper":"/paper/contrastive-continual-learning-with","title":"Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijy373/CCLIS","path":"CCLIS/util.py","file_url":"https://github.com/lijy373/CCLIS/blob/HEAD/CCLIS/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ec41d1d6c366550","mcp_get_code":{"code_sha256":"0ec41d1d6c366550"}},{"arxiv_id":"2403.03218","paper":"/paper/the-wmdp-benchmark-measuring-and-reducing","title":"The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"centerforaisafety/wmdp","path":"rmu/utils.py","file_url":"https://github.com/centerforaisafety/wmdp/blob/HEAD/rmu/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7bdf184a0ba57b39","mcp_get_code":{"code_sha256":"7bdf184a0ba57b39"}},{"arxiv_id":"2403.02817","paper":"/paper/here-comes-the-ai-worm-unleashing-zero-click","title":"Here Comes The AI Worm: Unleashing Zero-click Worms that Target GenAI-Powered Applications","date":null,"month_inferred_from_arxiv_id":"2024-03","title_source":"archive","repo":"stavc/compromptmized","path":"Legacy_Arxiv_V1/FlowSteering/llava/serve/model_worker.py","file_url":"https://github.com/stavc/compromptmized/blob/HEAD/Legacy_Arxiv_V1/FlowSteering/llava/serve/model_worker.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a2445eba62e3b93d","mcp_get_code":{"code_sha256":"a2445eba62e3b93d"}},{"arxiv_id":"2403.02690","paper":"/paper/dirichlet-based-per-sample-weighting-by","title":"Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaeHeeSun/RENT","path":"RENT/utils.py","file_url":"https://github.com/BaeHeeSun/RENT/blob/HEAD/RENT/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f0efc59c086c6a31","mcp_get_code":{"code_sha256":"f0efc59c086c6a31"}},{"arxiv_id":"2403.01632","paper":"/paper/improving-llm-code-generation-with-grammar","title":"SynCode: LLM Generation with Grammar Augmentation","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uiuc-focal-lab/syncode","path":"syncode/common.py","file_url":"https://github.com/uiuc-focal-lab/syncode/blob/HEAD/syncode/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c57ac9571ad01e54","mcp_get_code":{"code_sha256":"c57ac9571ad01e54"}},{"arxiv_id":"2403.01232","paper":"/paper/polynormer-polynomial-expressive-graph","title":"Polynormer: Polynomial-Expressive Graph Transformer in Linear Time","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cornell-zhang/Polynormer","path":"logger.py","file_url":"https://github.com/cornell-zhang/Polynormer/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"635fdf41b8d70789","mcp_get_code":{"code_sha256":"635fdf41b8d70789"}},{"arxiv_id":"2403.00742","paper":"/paper/dialect-prejudice-predicts-ai-decisions-about","title":"Dialect prejudice predicts AI decisions about people's character, employability, and criminality","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valentinhofmann/dialect-prejudice","path":"probing/helpers.py","file_url":"https://github.com/valentinhofmann/dialect-prejudice/blob/HEAD/probing/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a42f0ed885a6566","mcp_get_code":{"code_sha256":"9a42f0ed885a6566"}},{"arxiv_id":"2402.19465","paper":"/paper/towards-tracing-trustworthiness-dynamics","title":"Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chnq/tracingllm","path":"src/generate_activations.py","file_url":"https://github.com/chnq/tracingllm/blob/HEAD/src/generate_activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e1c7e2781771e313","mcp_get_code":{"code_sha256":"e1c7e2781771e313"}},{"arxiv_id":"2402.19455","paper":"/paper/listening-to-the-noise-blind-denoising-with","title":"Listening to the Noise: Blind Denoising with Gibbs Diffusion","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rubenohana/gibbs-diffusion","path":"gdiff/model.py","file_url":"https://github.com/rubenohana/gibbs-diffusion/blob/HEAD/gdiff/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c1ba3a81897dde8b","mcp_get_code":{"code_sha256":"c1ba3a81897dde8b"}},{"arxiv_id":"2402.18344","paper":"/paper/focus-on-your-question-interpreting-and","title":"Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BugMakerzzz/toxic_cot","path":"intervention_model.py","file_url":"https://github.com/BugMakerzzz/toxic_cot/blob/HEAD/intervention_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c4e2d401b9513a7","mcp_get_code":{"code_sha256":"1c4e2d401b9513a7"}},{"arxiv_id":"2402.18059","paper":"/paper/token-specific-watermarking-with-enhanced","title":"Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mignonjia/ts_watermark","path":"utils/generation.py","file_url":"https://github.com/mignonjia/ts_watermark/blob/HEAD/utils/generation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d0aa0b282b57db6","mcp_get_code":{"code_sha256":"8d0aa0b282b57db6"}},{"arxiv_id":"2402.16914","paper":"/paper/drattack-prompt-decomposition-and","title":"DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xirui-li/drattack","path":"attack_prompt_data/uncensored_vicuna/uncensor.py","file_url":"https://github.com/xirui-li/drattack/blob/HEAD/attack_prompt_data/uncensored_vicuna/uncensor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25f2fc9122340697","mcp_get_code":{"code_sha256":"25f2fc9122340697"}},{"arxiv_id":"2402.15300","paper":"/paper/seeing-is-believing-mitigating-hallucination","title":"Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"d-ailin/clip-guided-decoding","path":"lib/utils.py","file_url":"https://github.com/d-ailin/clip-guided-decoding/blob/HEAD/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac4396da119cebfe","mcp_get_code":{"code_sha256":"ac4396da119cebfe"}},{"arxiv_id":"2402.15131","paper":"/paper/interactive-kbqa-multi-turn-interactions-for","title":"Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimxionggm/interactive-kbqa","path":"predict/dialog_predictor.py","file_url":"https://github.com/jimxionggm/interactive-kbqa/blob/HEAD/predict/dialog_predictor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1bfcd9f0963c988d","mcp_get_code":{"code_sha256":"1bfcd9f0963c988d"}},{"arxiv_id":"2402.15043","paper":"/paper/kieval-a-knowledge-grounded-interactive","title":"KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuohaoyu/kieval","path":"kieval/models/local_hf_model.py","file_url":"https://github.com/zhuohaoyu/kieval/blob/HEAD/kieval/models/local_hf_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36e7c84d050efc4f","mcp_get_code":{"code_sha256":"36e7c84d050efc4f"}},{"arxiv_id":"2402.15018","paper":"/paper/unintended-impacts-of-llm-alignment-on-global","title":"Unintended Impacts of LLM Alignment on Global Representation","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salt-nlp/unintended-impacts-of-alignment","path":"code/ask-reddit-perplexity-testing.py","file_url":"https://github.com/salt-nlp/unintended-impacts-of-alignment/blob/HEAD/code/ask-reddit-perplexity-testing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53bbc4b31f140ab9","mcp_get_code":{"code_sha256":"53bbc4b31f140ab9"}},{"arxiv_id":"2402.15018","paper":"/paper/unintended-impacts-of-llm-alignment-on-global","title":"Unintended Impacts of LLM Alignment on Global Representation","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salt-nlp/unintended-impacts-of-alignment","path":"code/ask-reddit-reward-testing.py","file_url":"https://github.com/salt-nlp/unintended-impacts-of-alignment/blob/HEAD/code/ask-reddit-reward-testing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09fad9c068cf3e99","mcp_get_code":{"code_sha256":"09fad9c068cf3e99"}},{"arxiv_id":"2402.13494","paper":"/paper/gradsafe-detecting-unsafe-prompts-for-llms","title":"GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyq7/GradSafe","path":"code/find_critical_parameters.py","file_url":"https://github.com/xyq7/GradSafe/blob/HEAD/code/find_critical_parameters.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0763b555a75310d5","mcp_get_code":{"code_sha256":"0763b555a75310d5"}},{"arxiv_id":"2402.12483","paper":"/paper/artifacts-or-abduction-how-do-llms-answer","title":"Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nbalepur/mcqa-artifacts","path":"model/run_hf.py","file_url":"https://github.com/nbalepur/mcqa-artifacts/blob/HEAD/model/run_hf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a9ee8ba4a546e084","mcp_get_code":{"code_sha256":"a9ee8ba4a546e084"}},{"arxiv_id":"2402.12483","paper":"/paper/artifacts-or-abduction-how-do-llms-answer","title":"Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nbalepur/mcqa-artifacts","path":"model/run_hf_question_gen.py","file_url":"https://github.com/nbalepur/mcqa-artifacts/blob/HEAD/model/run_hf_question_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47a4372cd0ec5977","mcp_get_code":{"code_sha256":"47a4372cd0ec5977"}},{"arxiv_id":"2402.12483","paper":"/paper/artifacts-or-abduction-how-do-llms-answer","title":"Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nbalepur/mcqa-artifacts","path":"model/run_hf_question_gen_remote.py","file_url":"https://github.com/nbalepur/mcqa-artifacts/blob/HEAD/model/run_hf_question_gen_remote.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fdf4f61a71db951","mcp_get_code":{"code_sha256":"9fdf4f61a71db951"}},{"arxiv_id":"2402.10259","paper":"/paper/gaussianobject-just-taking-four-images-to-get","title":"GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting","date":"2024-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GaussianObject/GaussianObject","path":"mast3r/model.py","file_url":"https://github.com/GaussianObject/GaussianObject/blob/HEAD/mast3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e3f140b86bb9a08","mcp_get_code":{"code_sha256":"1e3f140b86bb9a08"}},{"arxiv_id":"2402.09773","paper":"/paper/nuteprune-efficient-progressive-pruning-with","title":"NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models","date":"2024-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucius-lsr/nuteprune","path":"utils/nuteprune_utils.py","file_url":"https://github.com/lucius-lsr/nuteprune/blob/HEAD/utils/nuteprune_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9c47a629adf4a03d","mcp_get_code":{"code_sha256":"9c47a629adf4a03d"}},{"arxiv_id":"2402.09497","paper":"/paper/instruction-tuning-for-secure-code-generation","title":"Instruction Tuning for Secure Code Generation","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eth-sri/safecoder","path":"safecoder/utils.py","file_url":"https://github.com/eth-sri/safecoder/blob/HEAD/safecoder/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a477d8eb91a8ac1b","mcp_get_code":{"code_sha256":"a477d8eb91a8ac1b"}},{"arxiv_id":"2402.09303","paper":"/paper/immediate-generalisation-in-humans-but-a","title":"Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lag","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wichmann-lab/supervised-learning-dynamics","path":"dnns/helper.py","file_url":"https://github.com/wichmann-lab/supervised-learning-dynamics/blob/HEAD/dnns/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ca2f0a7db8dc2aea","mcp_get_code":{"code_sha256":"ca2f0a7db8dc2aea"}},{"arxiv_id":"2402.09259","paper":"/paper/syntaxshap-syntax-aware-explainability-method","title":"SyntaxShap: Syntax-aware Explainability Method for Text Generation","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"k-amara/syntax-shap","path":"syntaxshap/model.py","file_url":"https://github.com/k-amara/syntax-shap/blob/HEAD/syntaxshap/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"473e8cfc361163dc","mcp_get_code":{"code_sha256":"473e8cfc361163dc"}},{"arxiv_id":"2402.08280","paper":"/paper/pix2code-learning-to-compose-neural-visual","title":"Pix2Code: Learning to Compose Neural Visual Concepts as Programs","date":"2024-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"3d4bf01aa4c60523","mcp_get_code":{"code_sha256":"3d4bf01aa4c60523"}},{"arxiv_id":"2402.07384","paper":"/paper/exploring-perceptual-limitation-of-multimodal","title":"Exploring Perceptual Limitation of Multimodal Large Language Models","date":"2024-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"saccharomycetes/mllm-perceptual-limitation","path":"src/models.py","file_url":"https://github.com/saccharomycetes/mllm-perceptual-limitation/blob/HEAD/src/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0a6c3693d2d3a8bb","mcp_get_code":{"code_sha256":"0a6c3693d2d3a8bb"}},{"arxiv_id":"2402.06155","paper":"/paper/model-editing-with-canonical-examples","title":"Model Editing with Canonical Examples","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"john-hewitt/model-editing-canonical-examples","path":"utils.py","file_url":"https://github.com/john-hewitt/model-editing-canonical-examples/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"10a192294fdc723a","mcp_get_code":{"code_sha256":"10a192294fdc723a"}},{"arxiv_id":"2402.03757","paper":"/paper/the-instinctive-bias-spurious-images-lead-to","title":"The Instinctive Bias: Spurious Images lead to Illusion in MLLMs","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MasaiahHan/CorrelationQA","path":"eval_llava.py","file_url":"https://github.com/MasaiahHan/CorrelationQA/blob/HEAD/eval_llava.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cad948aa6ecde096","mcp_get_code":{"code_sha256":"cad948aa6ecde096"}},{"arxiv_id":"2402.01929","paper":"/paper/sample-estimate-aggregate-a-recipe-for-causal","title":"Sample, estimate, aggregate: A recipe for causal discovery foundation models","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rmwu/sea","path":"src/model/factory.py","file_url":"https://github.com/rmwu/sea/blob/HEAD/src/model/factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"599cc4499ffcf6d0","mcp_get_code":{"code_sha256":"599cc4499ffcf6d0"}},{"arxiv_id":"2402.01702","paper":"/paper/fluent-dreaming-for-language-models","title":"Fluent dreaming for language models","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"confirm-solutions/dreamy","path":"dreamy/epo.py","file_url":"https://github.com/confirm-solutions/dreamy/blob/HEAD/dreamy/epo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70da85a2f1e954b0","mcp_get_code":{"code_sha256":"70da85a2f1e954b0"}},{"arxiv_id":"2401.13927","paper":"/paper/adaptive-text-watermark-for-large-language","title":"Adaptive Text Watermark for Large Language Models","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yepengliu/adaptive-text-watermark","path":"utils.py","file_url":"https://github.com/yepengliu/adaptive-text-watermark/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3f8eb1077803587f","mcp_get_code":{"code_sha256":"3f8eb1077803587f"}},{"arxiv_id":"2401.13856","paper":"/paper/laa-net-localized-artifact-attention-network","title":"LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"10ring/laa-net","path":"models/utils.py","file_url":"https://github.com/10ring/laa-net/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e59ae2c29ff112ff","mcp_get_code":{"code_sha256":"e59ae2c29ff112ff"}},{"arxiv_id":"2401.13296","paper":"/paper/visual-objectification-in-films-towards-a-new","title":"Visual Objectification in Films: Towards a New AI Task for Video Interpretation","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"husky-helen/ObyGaze12","path":"FeatureExtraction/utils_models.py","file_url":"https://github.com/husky-helen/ObyGaze12/blob/HEAD/FeatureExtraction/utils_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5230cc6a6a1706bc","mcp_get_code":{"code_sha256":"5230cc6a6a1706bc"}},{"arxiv_id":"2401.10440","paper":"/paper/breaking-the-curse-of-multilinguality-with","title":"Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blvns/x-elm","path":"metaseq/sequence_scorer_btm.py","file_url":"https://github.com/blvns/x-elm/blob/HEAD/metaseq/sequence_scorer_btm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5ec2c0476e20add","mcp_get_code":{"code_sha256":"e5ec2c0476e20add"}},{"arxiv_id":"2401.09881","paper":"/paper/ga-smaat-gnet-generative-adversarial-small","title":"GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eloyreulen/ga-smaat-gnet","path":"grad-cam.py","file_url":"https://github.com/eloyreulen/ga-smaat-gnet/blob/HEAD/grad-cam.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"85526a7c3b9677f0","mcp_get_code":{"code_sha256":"85526a7c3b9677f0"}},{"arxiv_id":"2401.05054","paper":"/paper/generating-diverse-and-high-quality-texts-by","title":"Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding","date":"2024-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CyberAgentAILab/diverse-mbr","path":"mbr/utils.py","file_url":"https://github.com/CyberAgentAILab/diverse-mbr/blob/HEAD/mbr/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"976148b6f8d28404","mcp_get_code":{"code_sha256":"976148b6f8d28404"}},{"arxiv_id":"2401.03497","paper":"/paper/eat-self-supervised-pre-training-with","title":"EAT: Self-Supervised Pre-Training with Efficient Audio Transformer","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwx-worst-one/eat","path":"feature_extract/feature_extract.py","file_url":"https://github.com/cwx-worst-one/eat/blob/HEAD/feature_extract/feature_extract.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a663b7a41d863edb","mcp_get_code":{"code_sha256":"a663b7a41d863edb"}},{"arxiv_id":"2401.03497","paper":"/paper/eat-self-supervised-pre-training-with","title":"EAT: Self-Supervised Pre-Training with Efficient Audio Transformer","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwx-worst-one/eat","path":"inference/inference.py","file_url":"https://github.com/cwx-worst-one/eat/blob/HEAD/inference/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31694976f9ae8e78","mcp_get_code":{"code_sha256":"31694976f9ae8e78"}},{"arxiv_id":"2401.02335","paper":"/paper/linguistic-profiling-of-deepfakes-an-open","title":"Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dflip3k/dflip-3k","path":"utils/ASimilarityCalculatior.py","file_url":"https://github.com/dflip3k/dflip-3k/blob/HEAD/utils/ASimilarityCalculatior.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df55583b9c1822dd","mcp_get_code":{"code_sha256":"df55583b9c1822dd"}},{"arxiv_id":"2401.00996","paper":"/paper/safety-and-performance-why-not-both-bi-1","title":"Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment","date":"2024-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"56d752872d178efc","mcp_get_code":{"code_sha256":"56d752872d178efc"}},{"arxiv_id":"2401.00996","paper":"/paper/safety-and-performance-why-not-both-bi-1","title":"Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment","date":"2024-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7ee32a20fafbdd50","mcp_get_code":{"code_sha256":"7ee32a20fafbdd50"}},{"arxiv_id":"2312.15166","paper":"/paper/solar-10-7b-scaling-large-language-models","title":"SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling","date":"2023-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jquesnelle/yarn","path":"eval/model_loader.py","file_url":"https://github.com/jquesnelle/yarn/blob/HEAD/eval/model_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7dc433c456d9abe2","mcp_get_code":{"code_sha256":"7dc433c456d9abe2"}},{"arxiv_id":"2312.14132","paper":"/paper/dust3r-geometric-3d-vision-made-easy","title":"DUSt3R: Geometric 3D Vision Made Easy","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver/dust3r","path":"dust3r/model.py","file_url":"https://github.com/naver/dust3r/blob/HEAD/dust3r/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e3f140b86bb9a08","mcp_get_code":{"code_sha256":"1e3f140b86bb9a08"}},{"arxiv_id":"2312.13772","paper":"/paper/on-task-performance-and-model-calibration","title":"On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridgeltl/ensembled-sicl","path":"utils/load_model.py","file_url":"https://github.com/cambridgeltl/ensembled-sicl/blob/HEAD/utils/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ae9e05f3029c194","mcp_get_code":{"code_sha256":"4ae9e05f3029c194"}},{"arxiv_id":"2312.06550","paper":"/paper/llm360-towards-fully-transparent-open-source","title":"LLM360: Towards Fully Transparent Open-Source LLMs","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llm360/analysis360","path":"analysis/memorization/utils/model_utils.py","file_url":"https://github.com/llm360/analysis360/blob/HEAD/analysis/memorization/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ca67b0968c8ad863","mcp_get_code":{"code_sha256":"ca67b0968c8ad863"}},{"arxiv_id":"2312.03701","paper":"/paper/self-conditioned-image-generation-via","title":"Return of Unconditional Generation: A Self-supervised Representation Generation Method","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LTH14/rcg","path":"pixel_generator/mage/models_mage.py","file_url":"https://github.com/LTH14/rcg/blob/HEAD/pixel_generator/mage/models_mage.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"43c459d4c7f134b2","mcp_get_code":{"code_sha256":"43c459d4c7f134b2"}},{"arxiv_id":"2312.00374","paper":"/paper/unleashing-cheapfakes-through-trojan-plugins","title":"The Philosopher's Stone: Trojaning Plugins of Large Language Models","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"chichidd/llm-lora-trojan","path":"inference.py","file_url":"https://github.com/chichidd/llm-lora-trojan/blob/HEAD/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"57bacd2b23ac0c92","mcp_get_code":{"code_sha256":"57bacd2b23ac0c92"}},{"arxiv_id":"2401.09424","paper":"/paper/precipitation-prediction-using-an-ensemble-of","title":"Precipitation Prediction Using an Ensemble of Lightweight Learners","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxz1217/weather4cast-2023-lxz","path":"train_stage1.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/train_stage1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ddeef0d06ed0d696","mcp_get_code":{"code_sha256":"ddeef0d06ed0d696"}},{"arxiv_id":"2311.17138","paper":"/paper/shadows-don-t-lie-and-lines-can-t-bend","title":"Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanlinm2/projective-geometry","path":"line_segment/lines_model.py","file_url":"https://github.com/hanlinm2/projective-geometry/blob/HEAD/line_segment/lines_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b419f2ecfa7997a","mcp_get_code":{"code_sha256":"6b419f2ecfa7997a"}},{"arxiv_id":"2311.17138","paper":"/paper/shadows-don-t-lie-and-lines-can-t-bend","title":"Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanlinm2/projective-geometry","path":"perspective_fields/fields_model.py","file_url":"https://github.com/hanlinm2/projective-geometry/blob/HEAD/perspective_fields/fields_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bde9121fab9d6344","mcp_get_code":{"code_sha256":"bde9121fab9d6344"}},{"arxiv_id":"2311.17138","paper":"/paper/shadows-don-t-lie-and-lines-can-t-bend","title":"Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanlinm2/projective-geometry","path":"prequalifier/prequalifier_model.py","file_url":"https://github.com/hanlinm2/projective-geometry/blob/HEAD/prequalifier/prequalifier_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9e6838b930e3168","mcp_get_code":{"code_sha256":"f9e6838b930e3168"}},{"arxiv_id":"2311.12904","paper":"/paper/learning-to-compute-grobner-bases","title":"Learning to Compute Gröbner Bases","date":"2023-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HiroshiKERA/transformer-groebner","path":"src/loader/model.py","file_url":"https://github.com/HiroshiKERA/transformer-groebner/blob/HEAD/src/loader/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f185d3c02bfd65e","mcp_get_code":{"code_sha256":"6f185d3c02bfd65e"}},{"arxiv_id":"2311.09774","paper":"/paper/huatuogpt-ii-one-stage-training-for-medical","title":"HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"freedomintelligence/huatuogpt-ii","path":"cli_demo.py","file_url":"https://github.com/freedomintelligence/huatuogpt-ii/blob/HEAD/cli_demo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"719c8f0c667de29a","mcp_get_code":{"code_sha256":"719c8f0c667de29a"}},{"arxiv_id":"2311.09731","paper":"/paper/prudent-silence-or-foolish-babble-examining","title":"Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"genglinliu/unknownbench","path":"src/run_llama_and_vicuna.py","file_url":"https://github.com/genglinliu/unknownbench/blob/HEAD/src/run_llama_and_vicuna.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e266763140afec2","mcp_get_code":{"code_sha256":"9e266763140afec2"}},{"arxiv_id":"2311.09476","paper":"/paper/ares-an-automated-evaluation-framework-for","title":"ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-futuredata/ares","path":"ares/LLM_as_a_Judge_Adaptation/Generate_Synthetic_Queries_and_Answers.py","file_url":"https://github.com/stanford-futuredata/ares/blob/HEAD/ares/LLM_as_a_Judge_Adaptation/Generate_Synthetic_Queries_and_Answers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0b7eddc443d1a412","mcp_get_code":{"code_sha256":"0b7eddc443d1a412"}},{"arxiv_id":"2311.07622","paper":"/paper/pretrain-like-you-inference-masked-tuning","title":"Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Chen-Junyang-cn/PLI","path":"genecis_eval.py","file_url":"https://github.com/Chen-Junyang-cn/PLI/blob/HEAD/genecis_eval.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6977e9b491f237fb","mcp_get_code":{"code_sha256":"6977e9b491f237fb"}},{"arxiv_id":"2311.06190","paper":"/paper/fouriergnn-rethinking-multivariate-time-1","title":"FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective","date":"2023-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aikunyi/fouriergnn","path":"utils/utils.py","file_url":"https://github.com/aikunyi/fouriergnn/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c42b2a10322b0dfd","mcp_get_code":{"code_sha256":"c42b2a10322b0dfd"}},{"arxiv_id":"2311.02061","paper":"/paper/active-learning-based-species-range-1","title":"Active Learning-Based Species Range Estimation","date":"2023-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chris-lange/sdm_active_sampling","path":"active_sampling/active_sampler.py","file_url":"https://github.com/chris-lange/sdm_active_sampling/blob/HEAD/active_sampling/active_sampler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ab97ad8522ea83e","mcp_get_code":{"code_sha256":"5ab97ad8522ea83e"}},{"arxiv_id":"2311.01452","paper":"/paper/time-series-anomaly-detection-using-diffusion","title":"Time Series Anomaly Detection using Diffusion-based Models","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fbrad/diffusionae","path":"DiffusionAE/train_diffusion_val.py","file_url":"https://github.com/fbrad/diffusionae/blob/HEAD/DiffusionAE/train_diffusion_val.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"098daffbfa69c989","mcp_get_code":{"code_sha256":"098daffbfa69c989"}},{"arxiv_id":"2310.18894","paper":null,"title":"arXiv:2310.18894","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"Crazy-Jack/nips2023_shape_vs_texture","path":"texture-synthesis-visualization/utilities.py","file_url":"https://github.com/Crazy-Jack/nips2023_shape_vs_texture/blob/HEAD/texture-synthesis-visualization/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0eae156a1d8475f9","mcp_get_code":{"code_sha256":"0eae156a1d8475f9"}},{"arxiv_id":"2310.18894","paper":null,"title":"arXiv:2310.18894","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"Crazy-Jack/nips2023_shape_vs_texture","path":"cnns-inference-top-k/model-vs-human_topK/modelvshuman/models/pytorch/shapenet/texture_shape_models.py","file_url":"https://github.com/Crazy-Jack/nips2023_shape_vs_texture/blob/HEAD/cnns-inference-top-k/model-vs-human_topK/modelvshuman/models/pytorch/shapenet/texture_shape_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"649f2e0c9cc9f64a","mcp_get_code":{"code_sha256":"649f2e0c9cc9f64a"}},{"arxiv_id":"2310.13590","paper":"/paper/relm-leveraging-language-models-for-enhanced","title":"ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syr-cn/relm","path":"model.py","file_url":"https://github.com/syr-cn/relm/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a0139633bd549f30","mcp_get_code":{"code_sha256":"a0139633bd549f30"}},{"arxiv_id":"2310.12344","paper":"/paper/lacma-language-aligning-contrastive-learning","title":"LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following","date":"2023-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joeyy5588/LACMA","path":"alfred/utils/model_util.py","file_url":"https://github.com/joeyy5588/LACMA/blob/HEAD/alfred/utils/model_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"026a1fec14fca72d","mcp_get_code":{"code_sha256":"026a1fec14fca72d"}},{"arxiv_id":"2310.08491","paper":"/paper/prometheus-inducing-fine-grained-evaluation","title":"Prometheus: Inducing Fine-grained Evaluation Capability in Language Models","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaistAI/Prometheus","path":"train/utils/model_utils.py","file_url":"https://github.com/kaistAI/Prometheus/blob/HEAD/train/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2752e49a6edeae4e","mcp_get_code":{"code_sha256":"2752e49a6edeae4e"}},{"arxiv_id":"2310.07289","paper":"/paper/beyond-factuality-a-comprehensive-evaluation","title":"Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChanLiang/CONNER","path":"src/relevance.py","file_url":"https://github.com/ChanLiang/CONNER/blob/HEAD/src/relevance.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3796d56b68dcd2be","mcp_get_code":{"code_sha256":"3796d56b68dcd2be"}},{"arxiv_id":"2310.07177","paper":"/paper/online-speculative-decoding","title":"Online Speculative Decoding","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxiaoxuanpku/osd","path":"distill/experiment/compare_model.py","file_url":"https://github.com/liuxiaoxuanpku/osd/blob/HEAD/distill/experiment/compare_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44894fa300cbe47f","mcp_get_code":{"code_sha256":"44894fa300cbe47f"}},{"arxiv_id":"2310.07177","paper":"/paper/online-speculative-decoding","title":"Online Speculative Decoding","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxiaoxuanpku/osd","path":"distill/experiment/compare_model_t5.py","file_url":"https://github.com/liuxiaoxuanpku/osd/blob/HEAD/distill/experiment/compare_model_t5.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e8be6f2c54dac94","mcp_get_code":{"code_sha256":"9e8be6f2c54dac94"}},{"arxiv_id":"2310.06498","paper":"/paper/a-new-benchmark-and-reverse-validation-method","title":"A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maybenotime/phd","path":"Ablation_Study_Llama2-7b/inference/model_utils.py","file_url":"https://github.com/maybenotime/phd/blob/HEAD/Ablation_Study_Llama2-7b/inference/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c7366fd47611b08f","mcp_get_code":{"code_sha256":"c7366fd47611b08f"}},{"arxiv_id":"2310.03668","paper":"/paper/gollie-annotation-guidelines-improve-zero","title":"GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitz-zentroa/GoLLIE","path":"src/model/load_model.py","file_url":"https://github.com/hitz-zentroa/GoLLIE/blob/HEAD/src/model/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bfca1181a79266f6","mcp_get_code":{"code_sha256":"bfca1181a79266f6"}},{"arxiv_id":"2310.02469","paper":"/paper/large-language-models-can-be-good-privacy","title":"PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yijia-xiao/pplm","path":"inference/model_utils.py","file_url":"https://github.com/yijia-xiao/pplm/blob/HEAD/inference/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"efea256714a4cb99","mcp_get_code":{"code_sha256":"efea256714a4cb99"}},{"arxiv_id":"2310.02207","paper":"/paper/language-models-represent-space-and-time","title":"Language Models Represent Space and Time","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wesg52/world-models","path":"load.py","file_url":"https://github.com/wesg52/world-models/blob/HEAD/load.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cd24389a9fa9621c","mcp_get_code":{"code_sha256":"cd24389a9fa9621c"}},{"arxiv_id":"2310.01468","paper":"/paper/the-entity-deduction-arena-a-playground-for","title":"Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-entity-deduction-arena","path":"utils.py","file_url":"https://github.com/apple/ml-entity-deduction-arena/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2eb507fb8f0e353b","mcp_get_code":{"code_sha256":"2eb507fb8f0e353b"}},{"arxiv_id":"2310.00526","paper":"/paper/are-graph-neural-networks-optimal","title":"Are Graph Neural Networks Optimal Approximation Algorithms?","date":"2023-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"penlu/bespoke-gnn4do","path":"model/saving.py","file_url":"https://github.com/penlu/bespoke-gnn4do/blob/HEAD/model/saving.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"87983881cde22896","mcp_get_code":{"code_sha256":"87983881cde22896"}},{"arxiv_id":"2310.00098","paper":"/paper/federated-learning-with-differential-privacy","title":"Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-pfl4asr","path":"pfl4asr/pfl4asr/utils/model.py","file_url":"https://github.com/apple/ml-pfl4asr/blob/HEAD/pfl4asr/pfl4asr/utils/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"44193dd0473ba550","mcp_get_code":{"code_sha256":"44193dd0473ba550"}},{"arxiv_id":"2309.16342","paper":"/paper/lagrangebench-a-lagrangian-fluid-mechanics-1","title":"LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RobDHess/Steerable-E3-GNN","path":"utils.py","file_url":"https://github.com/RobDHess/Steerable-E3-GNN/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8149b815c51038e","mcp_get_code":{"code_sha256":"a8149b815c51038e"}},{"arxiv_id":"2309.16240","paper":"/paper/beyond-reverse-kl-generalizing-direct","title":"Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alecwangcq/f-divergence-dpo","path":"mt_bench/convert_dpo_trainer_file_to_huggingface.py","file_url":"https://github.com/alecwangcq/f-divergence-dpo/blob/HEAD/mt_bench/convert_dpo_trainer_file_to_huggingface.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e9f2dd8699a6fff","mcp_get_code":{"code_sha256":"3e9f2dd8699a6fff"}},{"arxiv_id":"2309.15729","paper":"/paper/mindgpt-interpreting-what-you-see-with-non","title":"MindGPT: Interpreting What You See with Non-invasive Brain Recordings","date":"2023-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxuanc/mindgpt","path":"brain2text_infer.py","file_url":"https://github.com/jxuanc/mindgpt/blob/HEAD/brain2text_infer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b969e70a00d92cb4","mcp_get_code":{"code_sha256":"b969e70a00d92cb4"}},{"arxiv_id":"2309.13016","paper":"/paper/understanding-deep-gradient-leakage-via-1","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/inversion-influence-function","path":"baseline_utils.py","file_url":"https://github.com/illidanlab/inversion-influence-function/blob/HEAD/baseline_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"966417b0e5853e27","mcp_get_code":{"code_sha256":"966417b0e5853e27"}},{"arxiv_id":"2309.12288","paper":"/paper/the-reversal-curse-llms-trained-on-a-is-b","title":"The Reversal Curse: LLMs trained on \"A is B\" fail to learn \"B is A\"","date":"2023-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukasberglund/reversal_curse","path":"src/models/common.py","file_url":"https://github.com/lukasberglund/reversal_curse/blob/HEAD/src/models/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c9de75a7c39c5012","mcp_get_code":{"code_sha256":"c9de75a7c39c5012"}},{"arxiv_id":"2309.10105","paper":"/paper/understanding-catastrophic-forgetting-in","title":"Understanding Catastrophic Forgetting in Language Models via Implicit Inference","date":"2023-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kothasuhas/understanding-forgetting","path":"code_xnli/xnli.py","file_url":"https://github.com/kothasuhas/understanding-forgetting/blob/HEAD/code_xnli/xnli.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47a59cef57459cbd","mcp_get_code":{"code_sha256":"47a59cef57459cbd"}},{"arxiv_id":"2309.09836","paper":"/paper/recap-retrieval-augmented-audio-captioning","title":"RECAP: Retrieval-Augmented Audio Captioning","date":"2023-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sreyan88/recap","path":"infer.py","file_url":"https://github.com/sreyan88/recap/blob/HEAD/infer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7f44f9a5502ba96a","mcp_get_code":{"code_sha256":"7f44f9a5502ba96a"}},{"arxiv_id":"2309.08591","paper":"/paper/are-multilingual-llms-culturally-diverse","title":"Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings","date":"2023-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/maps","path":"experiments/src/model_collection.py","file_url":"https://github.com/UKPLab/maps/blob/HEAD/experiments/src/model_collection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4140916caf64322c","mcp_get_code":{"code_sha256":"4140916caf64322c"}},{"arxiv_id":"2309.05254","paper":"/paper/towards-better-data-exploitation-in-self","title":"Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuJF1226/BDEdepth","path":"evaluate_depth.py","file_url":"https://github.com/LiuJF1226/BDEdepth/blob/HEAD/evaluate_depth.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c27024ff816b4cdf","mcp_get_code":{"code_sha256":"c27024ff816b4cdf"}},{"arxiv_id":"2309.03452","paper":"/paper/multi-modality-guidance-network-for-missing","title":"Multimodal Guidance Network for Missing-Modality Inference in Content Moderation","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuokaizhao/multimodal-guidance-network","path":"MobileOne/finetune_mobileone_single.py","file_url":"https://github.com/zhuokaizhao/multimodal-guidance-network/blob/HEAD/MobileOne/finetune_mobileone_single.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83c7619610642884","mcp_get_code":{"code_sha256":"83c7619610642884"}},{"arxiv_id":"2309.03452","paper":"/paper/multi-modality-guidance-network-for-missing","title":"Multimodal Guidance Network for Missing-Modality Inference in Content Moderation","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuokaizhao/multimodal-guidance-network","path":"MobileOne/finetune_mobileone.py","file_url":"https://github.com/zhuokaizhao/multimodal-guidance-network/blob/HEAD/MobileOne/finetune_mobileone.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4a846c5456a0af7","mcp_get_code":{"code_sha256":"e4a846c5456a0af7"}},{"arxiv_id":"2309.00325","paper":"/paper/multi-fidelity-reduced-order-surrogate","title":"Multi-fidelity reduced-order surrogate modeling","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"contipaolo/multifidelity_pod","path":"multifidelity_NN_functions.py","file_url":"https://github.com/contipaolo/multifidelity_pod/blob/HEAD/multifidelity_NN_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e38f36188edb67f4","mcp_get_code":{"code_sha256":"e38f36188edb67f4"}},{"arxiv_id":"2308.16906","paper":"/paper/fine-grained-cross-view-geo-localization-1","title":"Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator","date":"2023-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xlwangdev/hc-net","path":"demo_gradio.py","file_url":"https://github.com/xlwangdev/hc-net/blob/HEAD/demo_gradio.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8404847f1ddcb0b","mcp_get_code":{"code_sha256":"e8404847f1ddcb0b"}},{"arxiv_id":"2308.16557","paper":"/paper/effective-test-generation-using-pre-trained","title":"Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing","date":null,"month_inferred_from_arxiv_id":"2023-08","title_source":"archive","repo":"expertisemodel/mutap","path":"llama_util/model_utils.py","file_url":"https://github.com/expertisemodel/mutap/blob/HEAD/llama_util/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bfc6d0ec102ca108","mcp_get_code":{"code_sha256":"bfc6d0ec102ca108"}},{"arxiv_id":"2308.11946","paper":"/paper/multi-scale-transformer-pyramid-networks-for","title":"Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GRYGY1215/MTPNet","path":"utils/tools.py","file_url":"https://github.com/GRYGY1215/MTPNet/blob/HEAD/utils/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dcdaa6f8115504d2","mcp_get_code":{"code_sha256":"dcdaa6f8115504d2"}},{"arxiv_id":"2308.11606","paper":"/paper/storybench-a-multifaceted-benchmark-for-1","title":"StoryBench: A Multifaceted Benchmark for Continuous Story Visualization","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/storybench","path":"metrics/models/InternVideo/internvideo.py","file_url":"https://github.com/google/storybench/blob/HEAD/metrics/models/InternVideo/internvideo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"06974c0cf54c1fbd","mcp_get_code":{"code_sha256":"06974c0cf54c1fbd"}},{"arxiv_id":"2308.11534","paper":"/paper/large-language-model-as-a-user-simulator","title":"PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FreedomIntelligence/PlatoLM","path":"model/sft_platolm/source/deploy/inference.py","file_url":"https://github.com/FreedomIntelligence/PlatoLM/blob/HEAD/model/sft_platolm/source/deploy/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5ccd04779e73683a","mcp_get_code":{"code_sha256":"5ccd04779e73683a"}},{"arxiv_id":"2308.10855","paper":"/paper/lateval-an-interactive-llms-evaluation","title":"LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thukelab/lateval","path":"chat_api/Llama2_chat/model_utils.py","file_url":"https://github.com/thukelab/lateval/blob/HEAD/chat_api/Llama2_chat/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a5139b11f2293744","mcp_get_code":{"code_sha256":"a5139b11f2293744"}},{"arxiv_id":"2308.09055","paper":"/paper/don-t-lose-the-message-while-paraphrasing-a","title":"Don't lose the message while paraphrasing: A study on content preserving style transfer","date":"2023-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"s-nlp/lewit-informal","path":"metrics/evaluation.py","file_url":"https://github.com/s-nlp/lewit-informal/blob/HEAD/metrics/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b039f50597b1f636","mcp_get_code":{"code_sha256":"b039f50597b1f636"}},{"arxiv_id":"2308.08231","paper":null,"title":"arXiv:2308.08231","date":null,"month_inferred_from_arxiv_id":"2023-08","title_source":null,"repo":"ZhangCYG/DDFHO","path":"nnutils/model_utils.py","file_url":"https://github.com/ZhangCYG/DDFHO/blob/HEAD/nnutils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e2a647df4a54935c","mcp_get_code":{"code_sha256":"e2a647df4a54935c"}},{"arxiv_id":"2308.05309","paper":"/paper/homophily-enhanced-structure-learning-for","title":"Homophily-enhanced Structure Learning for Graph Clustering","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"galogm/hole","path":"utils/utils.py","file_url":"https://github.com/galogm/hole/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1a31f4f074e05f4","mcp_get_code":{"code_sha256":"f1a31f4f074e05f4"}},{"arxiv_id":"2308.02097","paper":"/paper/multi-interactive-feature-learning-and-a-full","title":"Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"runjia0124/coconet","path":"models/P_loss.py","file_url":"https://github.com/runjia0124/coconet/blob/HEAD/models/P_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbfe8d248ce2ed69","mcp_get_code":{"code_sha256":"bbfe8d248ce2ed69"}},{"arxiv_id":"2307.13701","paper":"/paper/text-efo-k-cqa-towards-knowledge-graph","title":"$\\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation","date":"2023-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-knowcomp/efok-cqa","path":"QG_EFOX.py","file_url":"https://github.com/hkust-knowcomp/efok-cqa/blob/HEAD/QG_EFOX.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c08e440ba7f3e8c","mcp_get_code":{"code_sha256":"4c08e440ba7f3e8c"}},{"arxiv_id":"2307.09288","paper":"/paper/llama-2-open-foundation-and-fine-tuned-chat","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"squeezeailab/squeezellm","path":"squeezellm/model_parse.py","file_url":"https://github.com/squeezeailab/squeezellm/blob/HEAD/squeezellm/model_parse.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b66c43d90a1a3a1b","mcp_get_code":{"code_sha256":"b66c43d90a1a3a1b"}},{"arxiv_id":"2307.08208","paper":"/paper/towards-stealthy-backdoor-attacks-against","title":"Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanbocai/badspeech_soe","path":"backdoor_model_train.py","file_url":"https://github.com/hanbocai/badspeech_soe/blob/HEAD/backdoor_model_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0e071d99e755f8a","mcp_get_code":{"code_sha256":"e0e071d99e755f8a"}},{"arxiv_id":"2307.07664","paper":"/paper/outlier-detection-in-the-desi-bright-galaxy","title":"Outlier Detection in the DESI Bright Galaxy Survey","date":null,"month_inferred_from_arxiv_id":"2023-07","title_source":"archive","repo":"pmelchior/spender","path":"train/train_sdss.py","file_url":"https://github.com/pmelchior/spender/blob/HEAD/train/train_sdss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a3cdd668cf53501","mcp_get_code":{"code_sha256":"4a3cdd668cf53501"}},{"arxiv_id":"2307.05898","paper":"/paper/rectifying-noisy-labels-with-sequential-prior","title":"Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation","date":"2023-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beileicui/ms-tfal","path":"utils/LoadModel.py","file_url":"https://github.com/beileicui/ms-tfal/blob/HEAD/utils/LoadModel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3033b04079cdd932","mcp_get_code":{"code_sha256":"3033b04079cdd932"}},{"arxiv_id":"2307.03214","paper":"/paper/preadd-prefix-adaptive-decoding-for","title":"PREADD: Prefix-Adaptive Decoding for Controlled Text Generation","date":"2023-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jonnypei/acl23-preadd","path":"utils/model_server.py","file_url":"https://github.com/jonnypei/acl23-preadd/blob/HEAD/utils/model_server.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e621046299d54577","mcp_get_code":{"code_sha256":"e621046299d54577"}},{"arxiv_id":"2306.11134","paper":"/paper/openp5-benchmarking-foundation-models-for","title":"OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wenyueh/LLMforRS_item_representation","path":"utils.py","file_url":"https://github.com/Wenyueh/LLMforRS_item_representation/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a4b743b301d6af9","mcp_get_code":{"code_sha256":"3a4b743b301d6af9"}},{"arxiv_id":"2306.10619","paper":"/paper/towards-stability-of-autoregressive-neural","title":"Towards Stability of Autoregressive Neural Operators","date":"2023-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mikemccabe210/stabilizing_neural_operators","path":"inference/inference.py","file_url":"https://github.com/mikemccabe210/stabilizing_neural_operators/blob/HEAD/inference/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8544b9493fd511f","mcp_get_code":{"code_sha256":"f8544b9493fd511f"}},{"arxiv_id":"2306.07276","paper":"/paper/transcendental-idealism-of-planner-evaluating","title":"Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qcraftai/tip","path":"trans_idealism_planner/ti_planner.py","file_url":"https://github.com/qcraftai/tip/blob/HEAD/trans_idealism_planner/ti_planner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"18e8dbd6b83d9f7a","mcp_get_code":{"code_sha256":"18e8dbd6b83d9f7a"}},{"arxiv_id":"2306.05284","paper":"/paper/simple-and-controllable-music-generation","title":"Simple and Controllable Music Generation","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"collabora/whisperspeech","path":"whisperspeech/inference.py","file_url":"https://github.com/collabora/whisperspeech/blob/HEAD/whisperspeech/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e03ec9babe137321","mcp_get_code":{"code_sha256":"e03ec9babe137321"}},{"arxiv_id":"2306.00323","paper":"/paper/thought-cloning-learning-to-think-while-1","title":"Thought Cloning: Learning to Think while Acting by Imitating Human Thinking","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShengranHu/Thought-Cloning","path":"babyai/utils/model.py","file_url":"https://github.com/ShengranHu/Thought-Cloning/blob/HEAD/babyai/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63cced98b78ed47a","mcp_get_code":{"code_sha256":"63cced98b78ed47a"}},{"arxiv_id":"2305.13788","paper":"/paper/can-large-language-models-infer-and-disagree","title":"Can Large Language Models Capture Dissenting Human Voices?","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xfactlab/emnlp2023-LLM-Disagreement","path":"utils/models.py","file_url":"https://github.com/xfactlab/emnlp2023-LLM-Disagreement/blob/HEAD/utils/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df8175311a751726","mcp_get_code":{"code_sha256":"df8175311a751726"}},{"arxiv_id":"2305.13304","paper":"/paper/recurrentgpt-interactive-generation-of","title":"RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jackaduma/Recurrent-LLM","path":"models/baichuan_hf.py","file_url":"https://github.com/jackaduma/Recurrent-LLM/blob/HEAD/models/baichuan_hf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4a265aa4d0ce85c","mcp_get_code":{"code_sha256":"b4a265aa4d0ce85c"}},{"arxiv_id":"2305.13282","paper":"/paper/is-fine-tuning-needed-pre-trained-language","title":"Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Uppaal/lm-ood","path":"utils/model_utils.py","file_url":"https://github.com/Uppaal/lm-ood/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48775127b9ab8dbe","mcp_get_code":{"code_sha256":"48775127b9ab8dbe"}},{"arxiv_id":"2305.10973","paper":"/paper/drag-your-gan-interactive-point-based","title":"Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/internchat","path":"iGPT/models/husky.py","file_url":"https://github.com/opengvlab/internchat/blob/HEAD/iGPT/models/husky.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cbc8841e1bf04835","mcp_get_code":{"code_sha256":"cbc8841e1bf04835"}},{"arxiv_id":"2305.08379","paper":"/paper/tess-text-to-text-self-conditioned-simplex","title":"TESS: Text-to-Text Self-Conditioned Simplex Diffusion","date":"2023-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyh6560new/p3sum","path":"simplex-diffusion-main/sdlm/models/utils.py","file_url":"https://github.com/lyh6560new/p3sum/blob/HEAD/simplex-diffusion-main/sdlm/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea6d27b77f12d20f","mcp_get_code":{"code_sha256":"ea6d27b77f12d20f"}},{"arxiv_id":"2305.01644","paper":"/paper/key-locked-rank-one-editing-for-text-to-image","title":"Key-Locked Rank One Editing for Text-to-Image Personalization","date":"2023-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization","path":"clipseg/general_utils.py","file_url":"https://github.com/ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization/blob/HEAD/clipseg/general_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bca46b85e101b36d","mcp_get_code":{"code_sha256":"bca46b85e101b36d"}},{"arxiv_id":"2305.01140","paper":"/paper/geometric-latent-diffusion-models-for-3d","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","date":"2023-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minkaixu/geoldm","path":"utils.py","file_url":"https://github.com/minkaixu/geoldm/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc4da23dd75dcb46","mcp_get_code":{"code_sha256":"fc4da23dd75dcb46"}},{"arxiv_id":"2304.07063","paper":"/paper/on-existential-first-order-queries-inference","title":"Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-knowcomp/fit","path":"QG_FIT.py","file_url":"https://github.com/hkust-knowcomp/fit/blob/HEAD/QG_FIT.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c08e440ba7f3e8c","mcp_get_code":{"code_sha256":"4c08e440ba7f3e8c"}},{"arxiv_id":"2304.03378","paper":"/paper/self-supervised-video-similarity-learning","title":"Self-Supervised Video Similarity Learning","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gkordo/s2vs","path":"utils.py","file_url":"https://github.com/gkordo/s2vs/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cf2d32f293ea8590","mcp_get_code":{"code_sha256":"cf2d32f293ea8590"}},{"arxiv_id":"2303.17176","paper":"/paper/a-view-from-somewhere-human-centric-face","title":"A View From Somewhere: Human-Centric Face Representations","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yukimasano/PASS","path":"PASSify/face/convert_to_onnx.py","file_url":"https://github.com/yukimasano/PASS/blob/HEAD/PASSify/face/convert_to_onnx.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec9dcc3bf7178b3c","mcp_get_code":{"code_sha256":"ec9dcc3bf7178b3c"}},{"arxiv_id":"2303.15698","paper":"/paper/tfs-vit-token-level-feature-stylization-for","title":"TFS-ViT: Token-Level Feature Stylization for Domain Generalization","date":"2023-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mehrdad-noori/tfs-vit_token-level_feature_stylization","path":"domainbed/algorithms.py","file_url":"https://github.com/mehrdad-noori/tfs-vit_token-level_feature_stylization/blob/HEAD/domainbed/algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc20f931c8f71185","mcp_get_code":{"code_sha256":"cc20f931c8f71185"}},{"arxiv_id":"2303.14535","paper":"/paper/efficientad-accurate-visual-anomaly-detection","title":"EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ntkhoa95/EfficientAD","path":"inference.py","file_url":"https://github.com/ntkhoa95/EfficientAD/blob/HEAD/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a849040c6073d454","mcp_get_code":{"code_sha256":"a849040c6073d454"}},{"arxiv_id":"2303.14177","paper":"/paper/scaling-expert-language-models-with","title":"Scaling Expert Language Models with Unsupervised Domain Discovery","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kernelmachine/cbtm","path":"metaseq/scripts/train_clusterer.py","file_url":"https://github.com/kernelmachine/cbtm/blob/HEAD/metaseq/scripts/train_clusterer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b3f5ed16eed6f7f6","mcp_get_code":{"code_sha256":"b3f5ed16eed6f7f6"}},{"arxiv_id":"2303.12364","paper":"/paper/exbehrt-extended-transformer-for-electronic","title":"ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions","date":"2023-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepmedicine/BEHRT","path":"common/pytorch.py","file_url":"https://github.com/deepmedicine/BEHRT/blob/HEAD/common/pytorch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"28c0cc0d046b452f","mcp_get_code":{"code_sha256":"28c0cc0d046b452f"}},{"arxiv_id":"2303.04635","paper":"/paper/diffusing-gaussian-mixtures-for-generating","title":"Diffusing Gaussian Mixtures for Generating Categorical Data","date":"2023-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"networkslab/gmcd","path":"src/mutils.py","file_url":"https://github.com/networkslab/gmcd/blob/HEAD/src/mutils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b489ebfbb742d529","mcp_get_code":{"code_sha256":"b489ebfbb742d529"}},{"arxiv_id":"2302.13971","paper":"/paper/llama-open-and-efficient-foundation-language-1","title":"LLaMA: Open and Efficient Foundation Language Models","date":"2023-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fsoft-ai4code/codecapybara","path":"deploy/run_hf.py","file_url":"https://github.com/fsoft-ai4code/codecapybara/blob/HEAD/deploy/run_hf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bb9d66258a238b9c","mcp_get_code":{"code_sha256":"bb9d66258a238b9c"}},{"arxiv_id":"2302.01516","paper":"/paper/class-overwhelms-mutual-conditional-blended","title":"Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation","date":"2023-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation","path":"util.py","file_url":"https://github.com/Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a5905023bd0121fc","mcp_get_code":{"code_sha256":"a5905023bd0121fc"}},{"arxiv_id":"2301.08128","paper":"/paper/epic-gan-equivariant-point-cloud-generation","title":"EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets","date":"2023-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uhh-pd-ml/epic-gan","path":"generate.py","file_url":"https://github.com/uhh-pd-ml/epic-gan/blob/HEAD/generate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2e9b6433fce2fba8","mcp_get_code":{"code_sha256":"2e9b6433fce2fba8"}},{"arxiv_id":"2301.01456","paper":"/paper/audio-visual-efficient-conformer-for-robust","title":"Audio-Visual Efficient Conformer for Robust Speech Recognition","date":"2023-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"burchim/avec","path":"functions.py","file_url":"https://github.com/burchim/avec/blob/HEAD/functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"531a375057846fcd","mcp_get_code":{"code_sha256":"531a375057846fcd"}},{"arxiv_id":"2212.12794","paper":"/paper/graphcast-learning-skillful-medium-range","title":"GraphCast: Learning skillful medium-range global weather forecasting","date":"2022-12-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csubich/graphcast","path":"forecast/generate_model.py","file_url":"https://github.com/csubich/graphcast/blob/HEAD/forecast/generate_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a8056efbf2bfbd4d","mcp_get_code":{"code_sha256":"a8056efbf2bfbd4d"}},{"arxiv_id":"2211.13956","paper":"/paper/learning-general-audio-representations-with","title":"Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers","date":"2022-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kkoutini/passt_hear21","path":"hear21passt/base.py","file_url":"https://github.com/kkoutini/passt_hear21/blob/HEAD/hear21passt/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2f36edf707388db5","mcp_get_code":{"code_sha256":"2f36edf707388db5"}},{"arxiv_id":"2211.09912","paper":"/paper/do-graph-neural-networks-learn-traditional","title":"Do graph neural networks learn traditional jet substructure?","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"farakiko/xai4hep","path":"xai4hep/run_lrp_mlpf.py","file_url":"https://github.com/farakiko/xai4hep/blob/HEAD/xai4hep/run_lrp_mlpf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"abeff86539844fbf","mcp_get_code":{"code_sha256":"abeff86539844fbf"}},{"arxiv_id":"2211.01324","paper":"/paper/ediffi-text-to-image-diffusion-models-with-an","title":"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers","date":"2022-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cross-domain-compositing/cross-domain-compositing","path":"SVR/utils.py","file_url":"https://github.com/cross-domain-compositing/cross-domain-compositing/blob/HEAD/SVR/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f04426f214fbb1ec","mcp_get_code":{"code_sha256":"f04426f214fbb1ec"}},{"arxiv_id":"2210.14896","paper":"/paper/diffusiondb-a-large-scale-prompt-gallery","title":"DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models","date":"2022-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenjiqq/aesthetics-scorer","path":"aesthetics_scorer/model.py","file_url":"https://github.com/kenjiqq/aesthetics-scorer/blob/HEAD/aesthetics_scorer/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7772439645a30818","mcp_get_code":{"code_sha256":"7772439645a30818"}},{"arxiv_id":"2210.14896","paper":"/paper/diffusiondb-a-large-scale-prompt-gallery","title":"DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models","date":"2022-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenjiqq/aesthetics-scorer","path":"convnext_scorer/model.py","file_url":"https://github.com/kenjiqq/aesthetics-scorer/blob/HEAD/convnext_scorer/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3460308a8cb85ca3","mcp_get_code":{"code_sha256":"3460308a8cb85ca3"}},{"arxiv_id":"2210.06807","paper":"/paper/improving-out-of-distribution-generalization-1","title":"Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"novaglow646/nips22-mat-and-ldat-for-ood","path":"domainbed/algorithms.py","file_url":"https://github.com/novaglow646/nips22-mat-and-ldat-for-ood/blob/HEAD/domainbed/algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e62d0abc44b4927c","mcp_get_code":{"code_sha256":"e62d0abc44b4927c"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/avg_hubert_wav2vec2.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/avg_hubert_wav2vec2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bfab7ca773153d","mcp_get_code":{"code_sha256":"08bfab7ca773153d"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/cat_hubert_wav2vec2.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/cat_hubert_wav2vec2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a703abd08fd47f89","mcp_get_code":{"code_sha256":"a703abd08fd47f89"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/fusion_hubert_xlarge.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/fusion_hubert_xlarge.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ed6fbfce90ffb543","mcp_get_code":{"code_sha256":"ed6fbfce90ffb543"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/fusion_wav2vec2.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/fusion_wav2vec2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dafda41a2f9cc1a4","mcp_get_code":{"code_sha256":"dafda41a2f9cc1a4"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/hubert_xlarge.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/hubert_xlarge.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a7d26edcd882ce9","mcp_get_code":{"code_sha256":"2a7d26edcd882ce9"}},{"arxiv_id":"2209.12900","paper":"/paper/the-ability-of-self-supervised-speech-models","title":"The Efficacy of Self-Supervised Speech Models for Audio Representations","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/hear-2021-neurips-challenge---ntu-gura","path":"GURA/wav2vec2_large.py","file_url":"https://github.com/tony10101105/hear-2021-neurips-challenge---ntu-gura/blob/HEAD/GURA/wav2vec2_large.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c5899d2bde08515d","mcp_get_code":{"code_sha256":"c5899d2bde08515d"}},{"arxiv_id":"2209.04899","paper":"/paper/instruction-driven-history-aware-policies-for","title":"Instruction-driven history-aware policies for robotic manipulations","date":"2022-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guhur/hiveformer","path":"preprocess_instructions.py","file_url":"https://github.com/guhur/hiveformer/blob/HEAD/preprocess_instructions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eb3169facaa8376","mcp_get_code":{"code_sha256":"1eb3169facaa8376"}},{"arxiv_id":"2208.08270","paper":"/paper/on-the-privacy-effect-of-data-enhancement-via","title":"On the Privacy Effect of Data Enhancement via the Lens of Memorization","date":"2022-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lixiaothu/privacy_and_aug","path":"utils.py","file_url":"https://github.com/lixiaothu/privacy_and_aug/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0619c0c4d30c7511","mcp_get_code":{"code_sha256":"0619c0c4d30c7511"}},{"arxiv_id":"2208.05969","paper":"/paper/safety-and-performance-why-not-both-bi","title":"Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment","date":"2022-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiepku/safecompress","path":"mia_main.py","file_url":"https://github.com/jiepku/safecompress/blob/HEAD/mia_main.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"56d752872d178efc","mcp_get_code":{"code_sha256":"56d752872d178efc"}},{"arxiv_id":"2208.05969","paper":"/paper/safety-and-performance-why-not-both-bi","title":"Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment","date":"2022-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiepku/mia-safecompress","path":"mia_main.py","file_url":"https://github.com/jiepku/mia-safecompress/blob/HEAD/mia_main.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ee32a20fafbdd50","mcp_get_code":{"code_sha256":"7ee32a20fafbdd50"}},{"arxiv_id":"2208.05647","paper":"/paper/ppmn-pixel-phrase-matching-network-for-one","title":"PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding","date":"2022-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dzh19990407/ppmn","path":"models/extract_fpn_with_ckpt_load_from_detectron2.py","file_url":"https://github.com/dzh19990407/ppmn/blob/HEAD/models/extract_fpn_with_ckpt_load_from_detectron2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"68c73cc8252a81c8","mcp_get_code":{"code_sha256":"68c73cc8252a81c8"}},{"arxiv_id":"2207.14288","paper":"/paper/rewriting-geometric-rules-of-a-gan","title":"Rewriting Geometric Rules of a GAN","date":"2022-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peterwang512/ganwarping","path":"ganspace_edit.py","file_url":"https://github.com/peterwang512/ganwarping/blob/HEAD/ganspace_edit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"39a9a2e448bc9caf","mcp_get_code":{"code_sha256":"39a9a2e448bc9caf"}},{"arxiv_id":"2207.12080","paper":"/paper/intention-conditioned-long-term-human","title":"Intention-Conditioned Long-Term Human Egocentric Action Forecasting","date":"2022-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"evm7/ego4dlta-icvae","path":"H3M_predict.py","file_url":"https://github.com/evm7/ego4dlta-icvae/blob/HEAD/H3M_predict.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"066e42abdadd108a","mcp_get_code":{"code_sha256":"066e42abdadd108a"}},{"arxiv_id":"2207.09865","paper":"/paper/discrete-constrained-regression-for-local","title":"Discrete-Constrained Regression for Local Counting Models","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xhp-hust-2018-2011/dcreg","path":"utils/IOtools.py","file_url":"https://github.com/xhp-hust-2018-2011/dcreg/blob/HEAD/utils/IOtools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf8495bc6c35bf0e","mcp_get_code":{"code_sha256":"bf8495bc6c35bf0e"}},{"arxiv_id":"2207.03208","paper":"/paper/revisiting-pretraining-objectives-for-tabular","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kalelpark/DeepLearning-for-Tabular-Data","path":"model/common.py","file_url":"https://github.com/kalelpark/DeepLearning-for-Tabular-Data/blob/HEAD/model/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d13ea26797a378f5","mcp_get_code":{"code_sha256":"d13ea26797a378f5"}},{"arxiv_id":"2206.14774","paper":"/paper/tweetnlp-cutting-edge-natural-language","title":"TweetNLP: Cutting-Edge Natural Language Processing for Social Media","date":"2022-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cardiffnlp/tweetnlp","path":"tweetnlp/util.py","file_url":"https://github.com/cardiffnlp/tweetnlp/blob/HEAD/tweetnlp/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"12e9be32bca300f5","mcp_get_code":{"code_sha256":"12e9be32bca300f5"}},{"arxiv_id":"2206.13559","paper":"/paper/parameter-efficient-image-to-video-transfer","title":"ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning","date":"2022-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linziyi96/st-adapter","path":"utils.py","file_url":"https://github.com/linziyi96/st-adapter/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"319881416fb3b9dc","mcp_get_code":{"code_sha256":"319881416fb3b9dc"}},{"arxiv_id":"2206.04646","paper":"/paper/globally-optimal-algorithms-for-fixed-budged","title":"Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification","date":"2022-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsuchhiii/fixed-budget-bai","path":"pe_algorithms/mab/optnn.py","file_url":"https://github.com/tsuchhiii/fixed-budget-bai/blob/HEAD/pe_algorithms/mab/optnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9c9714cdfb608340","mcp_get_code":{"code_sha256":"9c9714cdfb608340"}},{"arxiv_id":"2205.15285","paper":"/paper/fast-dynamic-radiance-fields-with-time-aware","title":"Fast Dynamic Radiance Fields with Time-Aware Neural Voxels","date":"2022-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hustvl/TiNeuVox","path":"lib/utils.py","file_url":"https://github.com/hustvl/TiNeuVox/blob/HEAD/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1b418191a5e1a469","mcp_get_code":{"code_sha256":"1b418191a5e1a469"}},{"arxiv_id":"2205.12257","paper":"/paper/onepose-one-shot-object-pose-estimation","title":"OnePose: One-Shot Object Pose Estimation without CAD Models","date":"2022-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zju3dv/OnePose","path":"inference.py","file_url":"https://github.com/zju3dv/OnePose/blob/HEAD/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9a11f7aabe61f9e3","mcp_get_code":{"code_sha256":"9a11f7aabe61f9e3"}},{"arxiv_id":"2205.12134","paper":"/paper/adversarial-attack-on-attackers-post-process","title":"Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks","date":"2022-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sizhe-chen/aaa","path":"square.py","file_url":"https://github.com/sizhe-chen/aaa/blob/HEAD/square.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4fc8682d4d57cb91","mcp_get_code":{"code_sha256":"4fc8682d4d57cb91"}},{"arxiv_id":"2205.02593","paper":"/paper/metgen-a-module-based-entailment-tree","title":"METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation","date":"2022-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencent-ailab/metalogic","path":"code/inference.py","file_url":"https://github.com/tencent-ailab/metalogic/blob/HEAD/code/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb8883844e1d86f0","mcp_get_code":{"code_sha256":"bb8883844e1d86f0"}},{"arxiv_id":"2205.00865","paper":"/paper/weatherbench-probability-a-benchmark-dataset","title":"WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models","date":"2022-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sagar-garg/WeatherBench","path":"src/utils.py","file_url":"https://github.com/sagar-garg/WeatherBench/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5f098e3b46a99ef","mcp_get_code":{"code_sha256":"e5f098e3b46a99ef"}},{"arxiv_id":"2204.12489","paper":"/paper/sound-localization-by-self-supervised-time","title":"Sound Localization by Self-Supervised Time Delay Estimation","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IFICL/stereocrw","path":"utils/torch_utils.py","file_url":"https://github.com/IFICL/stereocrw/blob/HEAD/utils/torch_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd477c377ef9e596","mcp_get_code":{"code_sha256":"dd477c377ef9e596"}},{"arxiv_id":"2204.08397","paper":"/paper/fast-and-memory-efficient-network-towards","title":"Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution","date":"2022-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gaurav14cs17/Reparameterization-Denoising","path":"models/MFDNet.py","file_url":"https://github.com/Gaurav14cs17/Reparameterization-Denoising/blob/HEAD/models/MFDNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d37bd3a531577371","mcp_get_code":{"code_sha256":"d37bd3a531577371"}},{"arxiv_id":"2203.07540","paper":"/paper/scienceworld-is-your-agent-smarter-than-a-5th","title":"ScienceWorld: Is your Agent Smarter than a 5th Grader?","date":"2022-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/macaw","path":"macaw/utils.py","file_url":"https://github.com/allenai/macaw/blob/HEAD/macaw/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a96d409802f5ecdc","mcp_get_code":{"code_sha256":"a96d409802f5ecdc"}},{"arxiv_id":"2202.13711","paper":"/paper/evaluating-the-adversarial-robustness-of","title":"Evaluating the Adversarial Robustness of Adaptive Test-time Defenses","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/evaluating-adaptive-test-time-defenses","path":"shi_2020/models.py","file_url":"https://github.com/fra31/evaluating-adaptive-test-time-defenses/blob/HEAD/shi_2020/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2f90a0530f82d107","mcp_get_code":{"code_sha256":"2f90a0530f82d107"}},{"arxiv_id":"2202.11214","paper":"/paper/fourcastnet-a-global-data-driven-high","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","date":"2022-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nvlabs/fourcastnet","path":"inference/inference.py","file_url":"https://github.com/nvlabs/fourcastnet/blob/HEAD/inference/inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7db5894c5b296e18","mcp_get_code":{"code_sha256":"7db5894c5b296e18"}},{"arxiv_id":"2112.11641","paper":"/paper/jojogan-one-shot-face-stylization-1","title":"JoJoGAN: One Shot Face Stylization","date":"2021-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mchong6/JoJoGAN","path":"util.py","file_url":"https://github.com/mchong6/JoJoGAN/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a9dd0e4a021e4d0","mcp_get_code":{"code_sha256":"9a9dd0e4a021e4d0"}},{"arxiv_id":"2112.08168","paper":"/paper/boosting-neural-image-compression-for","title":"Boosting Neural Image Compression for Machines Using Latent Space Masking","date":"2021-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fau-lms/ncn_for_m2m","path":"ncn_model.py","file_url":"https://github.com/fau-lms/ncn_for_m2m/blob/HEAD/ncn_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"67b83d1f47e01c3f","mcp_get_code":{"code_sha256":"67b83d1f47e01c3f"}},{"arxiv_id":"2112.05253","paper":"/paper/magma-multimodal-augmentation-of-generative","title":"MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Aleph-Alpha/magma","path":"magma/utils.py","file_url":"https://github.com/Aleph-Alpha/magma/blob/HEAD/magma/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cda91d1c99cf9e9c","mcp_get_code":{"code_sha256":"cda91d1c99cf9e9c"}},{"arxiv_id":"2112.00955","paper":"/paper/source-free-unsupervised-graph-domain","title":"Source Free Unsupervised Graph Domain Adaptation","date":"2021-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haitaomao/soga","path":"codes/model/model_utilies.py","file_url":"https://github.com/haitaomao/soga/blob/HEAD/codes/model/model_utilies.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb21b2fbcd96a7ba","mcp_get_code":{"code_sha256":"bb21b2fbcd96a7ba"}},{"arxiv_id":"2111.12922","paper":"/paper/clustering-effect-of-linearized-adversarial","title":"Clustering Effect of (Linearized) Adversarial Robust Models","date":"2021-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bymavis/adv_weight_neurips2021","path":"evaluate_weight_correlation.py","file_url":"https://github.com/bymavis/adv_weight_neurips2021/blob/HEAD/evaluate_weight_correlation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1a518dcbc6d0778c","mcp_get_code":{"code_sha256":"1a518dcbc6d0778c"}},{"arxiv_id":"2111.05956","paper":"/paper/feature-generation-for-long-tail","title":"Feature Generation for Long-tail Classification","date":"2021-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rahulvigneswaran/tailcalibx","path":"libs/models/ResNext50Feature.py","file_url":"https://github.com/rahulvigneswaran/tailcalibx/blob/HEAD/libs/models/ResNext50Feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ba831f456a905ab","mcp_get_code":{"code_sha256":"1ba831f456a905ab"}},{"arxiv_id":"2111.01606","paper":"/paper/polytrack-tracking-with-bounding-polygons","title":"PolyTrack: Tracking with Bounding Polygons","date":"2021-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gafaua/PolyTrack","path":"src/lib/model/model.py","file_url":"https://github.com/gafaua/PolyTrack/blob/HEAD/src/lib/model/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7425309b038c87c","mcp_get_code":{"code_sha256":"d7425309b038c87c"}},{"arxiv_id":"2110.09057","paper":"/paper/training-deep-neural-networks-with-adaptive","title":"Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kentaroy47/vision-transformers-cifar10","path":"export_models.py","file_url":"https://github.com/kentaroy47/vision-transformers-cifar10/blob/HEAD/export_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"157f64840d3dfd7c","mcp_get_code":{"code_sha256":"157f64840d3dfd7c"}},{"arxiv_id":"2109.14509","paper":"/paper/pac-bayes-information-bottleneck","title":"PAC-Bayes Information Bottleneck","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanwangzf/pac-bayes-ib","path":"src/pib_utils.py","file_url":"https://github.com/ryanwangzf/pac-bayes-ib/blob/HEAD/src/pib_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e84c8fa1c587e30","mcp_get_code":{"code_sha256":"7e84c8fa1c587e30"}},{"arxiv_id":"2109.07622","paper":"/paper/towards-zero-shot-cross-lingual-image-1","title":"Towards Zero-shot Cross-lingual Image Retrieval and Tagging","date":"2021-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FreddeFrallan/Multilingual-CLIP","path":"multilingual_clip/legacy_multilingual_clip.py","file_url":"https://github.com/FreddeFrallan/Multilingual-CLIP/blob/HEAD/multilingual_clip/legacy_multilingual_clip.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"325e7d8398013efd","mcp_get_code":{"code_sha256":"325e7d8398013efd"}},{"arxiv_id":"2109.02221","paper":"/paper/nearest-neighbour-few-shot-learning-for-cross","title":"Nearest Neighbour Few-Shot Learning for Cross-lingual Classification","date":"2021-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/nearest-neighbor-crosslingual-classification","path":"models.py","file_url":"https://github.com/amazon-research/nearest-neighbor-crosslingual-classification/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2a755c99df3f125f","mcp_get_code":{"code_sha256":"2a755c99df3f125f"}},{"arxiv_id":"2106.15580","paper":"/paper/continuous-latent-process-flows","title":"Continuous Latent Process Flows","date":"2021-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"borealisai/continuous-latent-process-flows","path":"run_likelihood_estimation.py","file_url":"https://github.com/borealisai/continuous-latent-process-flows/blob/HEAD/run_likelihood_estimation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"10252326a09692d2","mcp_get_code":{"code_sha256":"10252326a09692d2"}},{"arxiv_id":"2106.09685","paper":"/paper/lora-low-rank-adaptation-of-large-language","title":"LoRA: Low-Rank Adaptation of Large Language Models","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitz-zentroa/this-is-not-a-dataset","path":"load_model.py","file_url":"https://github.com/hitz-zentroa/this-is-not-a-dataset/blob/HEAD/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"60b08148856c99db","mcp_get_code":{"code_sha256":"60b08148856c99db"}},{"arxiv_id":"2106.07597","paper":"/paper/mlperf-tiny-benchmark","title":"MLPerf Tiny Benchmark","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlcommons/tiny","path":"benchmark/training/anomaly_detection/keras_model.py","file_url":"https://github.com/mlcommons/tiny/blob/HEAD/benchmark/training/anomaly_detection/keras_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"62ef34c2f0401b44","mcp_get_code":{"code_sha256":"62ef34c2f0401b44"}},{"arxiv_id":"2106.04800","paper":"/paper/diffusion-source-identification-on-networks","title":"Diffusion Source Identification on Networks with Statistical Confidence","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lab-sigma/Diffusion-Source-Identification","path":"src/diffusion_source/infection_model.py","file_url":"https://github.com/lab-sigma/Diffusion-Source-Identification/blob/HEAD/src/diffusion_source/infection_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94957fd0def07c09","mcp_get_code":{"code_sha256":"94957fd0def07c09"}},{"arxiv_id":"2106.04781","paper":"/paper/embedding-physics-to-learn-spatiotemporal","title":"Encoding physics to learn reaction-diffusion processes","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Raocp/PeRCNN","path":"2d_burgers/train_2d_burgers.py","file_url":"https://github.com/Raocp/PeRCNN/blob/HEAD/2d_burgers/train_2d_burgers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"450eb656bf6f36b7","mcp_get_code":{"code_sha256":"450eb656bf6f36b7"}},{"arxiv_id":"2106.04781","paper":"/paper/embedding-physics-to-learn-spatiotemporal","title":"Encoding physics to learn reaction-diffusion processes","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Raocp/PeRCNN","path":"3d_gs_rd/train_3drd.py","file_url":"https://github.com/Raocp/PeRCNN/blob/HEAD/3d_gs_rd/train_3drd.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2bbb5bd944dabf78","mcp_get_code":{"code_sha256":"2bbb5bd944dabf78"}},{"arxiv_id":"2106.04732","paper":"/paper/adamatch-a-unified-approach-to-semi","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smkim7-kr/AdaMatch-pytorch","path":"models.py","file_url":"https://github.com/smkim7-kr/AdaMatch-pytorch/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33836088c3046a9e","mcp_get_code":{"code_sha256":"33836088c3046a9e"}},{"arxiv_id":"2106.03699","paper":"/paper/formalizing-distribution-inference-risks","title":"Formalizing Distribution Inference Risks","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iamgroot42/distribution_inference","path":"boneage/model_utils.py","file_url":"https://github.com/iamgroot42/distribution_inference/blob/HEAD/boneage/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d543ba1f0f69c7","mcp_get_code":{"code_sha256":"b0d543ba1f0f69c7"}},{"arxiv_id":"2106.02734","paper":"/paper/revisiting-hilbert-schmidt-information","title":"Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neu-spiral/hbar","path":"source/hbar/utils/io.py","file_url":"https://github.com/neu-spiral/hbar/blob/HEAD/source/hbar/utils/io.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14ba3f43b8e423c3","mcp_get_code":{"code_sha256":"14ba3f43b8e423c3"}},{"arxiv_id":"2105.13889","paper":"/paper/equilibrium-and-non-equilibrium-regimes-in","title":"Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AurelienDecelle/TorchRBM","path":"rbm/io.py","file_url":"https://github.com/AurelienDecelle/TorchRBM/blob/HEAD/rbm/io.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5119b421a1e356a4","mcp_get_code":{"code_sha256":"5119b421a1e356a4"}},{"arxiv_id":"2105.09109","paper":"/paper/an-orthogonal-classifier-for-improving-the","title":"An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks","date":"2021-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MTandHJ/roboc","path":"src/loadopts.py","file_url":"https://github.com/MTandHJ/roboc/blob/HEAD/src/loadopts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f3b694c6c2fed2b1","mcp_get_code":{"code_sha256":"f3b694c6c2fed2b1"}},{"arxiv_id":"2105.06453","paper":"/paper/episodic-transformer-for-vision-and-language","title":"Episodic Transformer for Vision-and-Language Navigation","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexpashevich/E.T.","path":"alfred/utils/model_util.py","file_url":"https://github.com/alexpashevich/E.T./blob/HEAD/alfred/utils/model_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"026a1fec14fca72d","mcp_get_code":{"code_sha256":"026a1fec14fca72d"}},{"arxiv_id":"2105.02605","paper":"/paper/graphformers-gnn-nested-language-models-for","title":"GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph","date":"2021-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/GraphFormers","path":"src/models/tnlrv3/convert_state_dict.py","file_url":"https://github.com/microsoft/GraphFormers/blob/HEAD/src/models/tnlrv3/convert_state_dict.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91d5980aeafe04e4","mcp_get_code":{"code_sha256":"91d5980aeafe04e4"}},{"arxiv_id":"2104.07916","paper":"/paper/polynomial-networks-in-deep-classifiers","title":"Augmenting Deep Classifiers with Polynomial Neural Networks","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grigorisg9gr/polynomials-for-augmenting-nns","path":"utils.py","file_url":"https://github.com/grigorisg9gr/polynomials-for-augmenting-nns/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"fc447d025b07e52a","mcp_get_code":{"code_sha256":"fc447d025b07e52a"}},{"arxiv_id":"2104.03344","paper":"/paper/ovanet-one-vs-all-network-for-universal","title":"OVANet: One-vs-All Network for Universal Domain Adaptation","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/OVANet","path":"utils/utils.py","file_url":"https://github.com/VisionLearningGroup/OVANet/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5fe84875f47cd9b","mcp_get_code":{"code_sha256":"e5fe84875f47cd9b"}},{"arxiv_id":"2103.15087","paper":"/paper/learning-a-sketch-tensor-space-for-image","title":"Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes","date":"2021-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ewrfcas/MST_inpainting","path":"src/training.py","file_url":"https://github.com/ewrfcas/MST_inpainting/blob/HEAD/src/training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f93fa196a0539bce","mcp_get_code":{"code_sha256":"f93fa196a0539bce"}},{"arxiv_id":"2010.07788","paper":"/paper/generalizing-universal-adversarial-attacks","title":"Generalizing Universal Adversarial Attacks Beyond Additive Perturbations","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TrustAI/DeepConcolic","path":"deepconcolic/dbnabstr.py","file_url":"https://github.com/TrustAI/DeepConcolic/blob/HEAD/deepconcolic/dbnabstr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"667e814e392d291e","mcp_get_code":{"code_sha256":"667e814e392d291e"}},{"arxiv_id":"2010.07788","paper":"/paper/generalizing-universal-adversarial-attacks","title":"Generalizing Universal Adversarial Attacks Beyond Additive Perturbations","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TrustAI/DeepConcolic","path":"deepconcolic/dbncXplore.py","file_url":"https://github.com/TrustAI/DeepConcolic/blob/HEAD/deepconcolic/dbncXplore.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"7823fdeea15cde56","mcp_get_code":{"code_sha256":"7823fdeea15cde56"}},{"arxiv_id":"2010.02855","paper":"/paper/curi-a-benchmark-for-productive-concept-1","title":"CURI: A Benchmark for Productive Concept Learning Under Uncertainty","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/productive_concept_learning","path":"hydra_train.py","file_url":"https://github.com/facebookresearch/productive_concept_learning/blob/HEAD/hydra_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3d4bf01aa4c60523","mcp_get_code":{"code_sha256":"3d4bf01aa4c60523"}},{"arxiv_id":"2010.02803","paper":"/paper/a-transformer-based-framework-for-1","title":"A Transformer-based Framework for Multivariate Time Series Representation Learning","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gzerveas/mvts_transformer","path":"src/utils/utils.py","file_url":"https://github.com/gzerveas/mvts_transformer/blob/HEAD/src/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06f548c9975c99a4","mcp_get_code":{"code_sha256":"06f548c9975c99a4"}},{"arxiv_id":"2009.04448","paper":"/paper/semi-supervised-medical-image-segmentation-1","title":"Semi-supervised Medical Image Segmentation through Dual-task Consistency","date":"2020-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HiLab-git/DTC","path":"code/utils/util.py","file_url":"https://github.com/HiLab-git/DTC/blob/HEAD/code/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"2008.03781","paper":"/paper/semeval-2020-task-8-memotion-analysis-the","title":"SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!","date":"2020-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"terenceylchow124/Meme-MultiModal","path":"utils/util.py","file_url":"https://github.com/terenceylchow124/Meme-MultiModal/blob/HEAD/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bef700d6e3bccf3a","mcp_get_code":{"code_sha256":"bef700d6e3bccf3a"}},{"arxiv_id":"2007.14628","paper":"/paper/solving-the-blind-perspective-n-point-problem","title":"Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liumouliu/Deep_blind_PnP","path":"lib/utils.py","file_url":"https://github.com/Liumouliu/Deep_blind_PnP/blob/HEAD/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cf0cddae7aa96a4b","mcp_get_code":{"code_sha256":"cf0cddae7aa96a4b"}},{"arxiv_id":"2007.12861","paper":"/paper/adversarial-privacy-preserving-filter","title":"Adversarial Privacy-preserving Filter","date":"2020-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adversarial-for-goodness/APF","path":"apf_pytorch.py","file_url":"https://github.com/adversarial-for-goodness/APF/blob/HEAD/apf_pytorch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"397486956e92c460","mcp_get_code":{"code_sha256":"397486956e92c460"}},{"arxiv_id":"2007.12770","paper":"/paper/babyai-1-1","title":"BabyAI 1.1","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mila-udem/babyai","path":"babyai/utils/model.py","file_url":"https://github.com/mila-udem/babyai/blob/HEAD/babyai/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"129723f98ac7a958","mcp_get_code":{"code_sha256":"129723f98ac7a958"}},{"arxiv_id":"2007.07453","paper":"/paper/graph-based-social-relation-reasoning","title":"Graph-Based Social Relation Reasoning","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Li-Wanhua/GR2N","path":"GRRN/SR_train.py","file_url":"https://github.com/Li-Wanhua/GR2N/blob/HEAD/GRRN/SR_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b2ea6152ba12a58","mcp_get_code":{"code_sha256":"0b2ea6152ba12a58"}},{"arxiv_id":"2007.06028","paper":"/paper/tera-self-supervised-learning-of-transformer","title":"TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech","date":"2020-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pandade1997/tera_asvproof","path":"transformer/model_dual.py","file_url":"https://github.com/Pandade1997/tera_asvproof/blob/HEAD/transformer/model_dual.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b8e06d04c4faf7f","mcp_get_code":{"code_sha256":"8b8e06d04c4faf7f"}},{"arxiv_id":"2007.02924","paper":"/paper/int-an-inequality-benchmark-for-evaluating","title":"INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albertqjiang/INT","path":"int_environment/visualization/model_debug.py","file_url":"https://github.com/albertqjiang/INT/blob/HEAD/int_environment/visualization/model_debug.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea3d9cdbcf7a9b4e","mcp_get_code":{"code_sha256":"ea3d9cdbcf7a9b4e"}},{"arxiv_id":"2006.14749","paper":"/paper/deepfake-detection-using-spatiotemporal","title":"Deepfake Detection using Spatiotemporal Convolutional Networks","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oidelima/Deepfake-Detection","path":"src/Pytorch_Retinaface/detect.py","file_url":"https://github.com/oidelima/Deepfake-Detection/blob/HEAD/src/Pytorch_Retinaface/detect.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c12f449e2cea71d1","mcp_get_code":{"code_sha256":"c12f449e2cea71d1"}},{"arxiv_id":"2006.14748","paper":"/paper/proper-network-interpretability-helps","title":"Proper Network Interpretability Helps Adversarial Robustness in Classification","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akhilanb/proper-interpretability","path":"funcs.py","file_url":"https://github.com/akhilanb/proper-interpretability/blob/HEAD/funcs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08cba5535c33775b","mcp_get_code":{"code_sha256":"08cba5535c33775b"}},{"arxiv_id":"2006.14748","paper":"/paper/proper-network-interpretability-helps","title":"Proper Network Interpretability Helps Adversarial Robustness in Classification","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AkhilanB/Proper-Interpretability","path":"funcs.py","file_url":"https://github.com/AkhilanB/Proper-Interpretability/blob/HEAD/funcs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a6fba14f7d4610ed","mcp_get_code":{"code_sha256":"a6fba14f7d4610ed"}},{"arxiv_id":"2006.09790","paper":"/paper/categorical-normalizing-flows-via-continuous","title":"Categorical Normalizing Flows via Continuous Transformations","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phlippe/CategoricalNF","path":"general/mutils.py","file_url":"https://github.com/phlippe/CategoricalNF/blob/HEAD/general/mutils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14bdea82d68b6932","mcp_get_code":{"code_sha256":"14bdea82d68b6932"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbzuai-paris/lm-evaluation-harness-atlas-chat","path":"lm_eval/models/nemo_lm.py","file_url":"https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat/blob/HEAD/lm_eval/models/nemo_lm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ff6d4e326d3c0db","mcp_get_code":{"code_sha256":"4ff6d4e326d3c0db"}},{"arxiv_id":"2005.13899","paper":"/paper/deep-learning-for-automatic-pneumonia","title":"Deep Learning for Automatic Pneumonia Detection","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tatigabru/kaggle-rsna","path":"src/predict.py","file_url":"https://github.com/tatigabru/kaggle-rsna/blob/HEAD/src/predict.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e71f23abf96a7802","mcp_get_code":{"code_sha256":"e71f23abf96a7802"}},{"arxiv_id":"2005.09623","paper":"/paper/focus-on-defocus-bridging-the-synthetic-to","title":"Focus on defocus: bridging the synthetic to real domain gap for depth estimation","date":"2020-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvl-tum/defocus-net","path":"source/util_func.py","file_url":"https://github.com/dvl-tum/defocus-net/blob/HEAD/source/util_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"319e01c0893cd02e","mcp_get_code":{"code_sha256":"319e01c0893cd02e"}},{"arxiv_id":"2005.09544","paper":"/paper/ciagan-conditional-identity-anonymization","title":"CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks","date":"2020-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvl-tum/ciagan","path":"source/util_func.py","file_url":"https://github.com/dvl-tum/ciagan/blob/HEAD/source/util_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33793db2894b8239","mcp_get_code":{"code_sha256":"33793db2894b8239"}},{"arxiv_id":"2005.09007","paper":"/paper/u-2-net-going-deeper-with-nested-u-structure","title":"U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"singhaman092/rembg","path":"src/rembg/u2net/detect.py","file_url":"https://github.com/singhaman092/rembg/blob/HEAD/src/rembg/u2net/detect.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"82b06ae41a637f16","mcp_get_code":{"code_sha256":"82b06ae41a637f16"}},{"arxiv_id":"2004.08532","paper":"/paper/dgl-ke-training-knowledge-graph-embeddings-at","title":"DGL-KE: Training Knowledge Graph Embeddings at Scale","date":null,"month_inferred_from_arxiv_id":"2020-04","title_source":"archive","repo":"awslabs/dgl-ke","path":"python/dglke/train_mxnet.py","file_url":"https://github.com/awslabs/dgl-ke/blob/HEAD/python/dglke/train_mxnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"099352c8df6bbfe5","mcp_get_code":{"code_sha256":"099352c8df6bbfe5"}},{"arxiv_id":"2004.01888","paper":"/paper/a-simple-baseline-for-multi-object-tracking","title":"FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking","date":"2020-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FlorentijnD/FairMOT","path":"src/lib/models/model.py","file_url":"https://github.com/FlorentijnD/FairMOT/blob/HEAD/src/lib/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42af9659af852ebd","mcp_get_code":{"code_sha256":"42af9659af852ebd"}},{"arxiv_id":"2004.01177","paper":"/paper/tracking-objects-as-points","title":"Tracking Objects as Points","date":"2020-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingyizhou/CenterTrack","path":"src/lib/model/model.py","file_url":"https://github.com/xingyizhou/CenterTrack/blob/HEAD/src/lib/model/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7425309b038c87c","mcp_get_code":{"code_sha256":"d7425309b038c87c"}},{"arxiv_id":"2004.01177","paper":"/paper/tracking-objects-as-points","title":"Tracking Objects as Points","date":"2020-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qa276390/searchtrack","path":"src/lib/model/model.py","file_url":"https://github.com/qa276390/searchtrack/blob/HEAD/src/lib/model/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0dcae8d7c7f7659","mcp_get_code":{"code_sha256":"b0dcae8d7c7f7659"}},{"arxiv_id":"2003.09005","paper":"/paper/semi-supervised-semantic-segmentation-with-1","title":"Semi-Supervised Semantic Segmentation with Cross-Consistency Training","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Luoxd1996/DTC","path":"code/utils/util.py","file_url":"https://github.com/Luoxd1996/DTC/blob/HEAD/code/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"2003.08608","paper":"/paper/depth-potentiality-aware-gated-attention","title":"DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JosephChenHub/DPANet","path":"lib/utils.py","file_url":"https://github.com/JosephChenHub/DPANet/blob/HEAD/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7883c98c8e661a2b","mcp_get_code":{"code_sha256":"7883c98c8e661a2b"}},{"arxiv_id":"2003.03877","paper":"/paper/focl-feature-oriented-continual-learning-for","title":"FoCL: Feature-Oriented Continual Learning for Generative Models","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nvcuong/variational-continual-learning","path":"dgm/load_classifier.py","file_url":"https://github.com/nvcuong/variational-continual-learning/blob/HEAD/dgm/load_classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2934a0c8b74e96f9","mcp_get_code":{"code_sha256":"2934a0c8b74e96f9"}},{"arxiv_id":"2003.03836","paper":"/paper/fine-grained-visual-classification-via","title":"Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PRIS-CV/PMG-Progressive-Multi-Granularity-Training","path":"utils.py","file_url":"https://github.com/PRIS-CV/PMG-Progressive-Multi-Granularity-Training/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e930d1ccdd527d08","mcp_get_code":{"code_sha256":"e930d1ccdd527d08"}},{"arxiv_id":"2002.12530","paper":"/paper/temporal-convolutional-attention-based","title":"Temporal Convolutional Attention-based Network For Sequence Modeling","date":"2020-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haohy/TCAN","path":"utils/utils.py","file_url":"https://github.com/haohy/TCAN/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d868669a776c8bd","mcp_get_code":{"code_sha256":"4d868669a776c8bd"}},{"arxiv_id":"2002.04745","paper":"/paper/on-layer-normalization-in-the-transformer-1","title":"On Layer Normalization in the Transformer Architecture","date":"2020-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"colorfulscoop/tfdlg","path":"tfdlg/utils.py","file_url":"https://github.com/colorfulscoop/tfdlg/blob/HEAD/tfdlg/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"932caee3efef6087","mcp_get_code":{"code_sha256":"932caee3efef6087"}},{"arxiv_id":"2001.06826","paper":"/paper/zero-reference-deep-curve-estimation-for-low","title":"Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement","date":"2020-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tuvovan/Zero_DCE_TF","path":"video-dce.py","file_url":"https://github.com/tuvovan/Zero_DCE_TF/blob/HEAD/video-dce.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"619e70eaae247e45","mcp_get_code":{"code_sha256":"619e70eaae247e45"}},{"arxiv_id":"2001.04608","paper":"/paper/actions-as-moving-points","title":"Actions as Moving Points","date":"2020-01-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MCG-NJU/MOC-Detector","path":"src/MOC_utils/model.py","file_url":"https://github.com/MCG-NJU/MOC-Detector/blob/HEAD/src/MOC_utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b32caffdac3e2e03","mcp_get_code":{"code_sha256":"b32caffdac3e2e03"}},{"arxiv_id":"2001.03994","paper":"/paper/fast-is-better-than-free-revisiting-1","title":"Fast is better than free: Revisiting adversarial training","date":"2020-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MetaSolver/icml2021","path":"sopa/src/models/utils.py","file_url":"https://github.com/MetaSolver/icml2021/blob/HEAD/sopa/src/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"7ceaa0e5bd758ab3","mcp_get_code":{"code_sha256":"7ceaa0e5bd758ab3"}},{"arxiv_id":"2001.01568","paper":"/paper/learned-image-compression-with-discretized","title":"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention","path":"model.py","file_url":"https://github.com/LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"67b83d1f47e01c3f","mcp_get_code":{"code_sha256":"67b83d1f47e01c3f"}},{"arxiv_id":"1911.09070","paper":"/paper/efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/efficientdet","path":"src/convert_weight.py","file_url":"https://github.com/lyqcom/efficientdet/blob/HEAD/src/convert_weight.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6b0e7aeddc8d13b6","mcp_get_code":{"code_sha256":"6b0e7aeddc8d13b6"}},{"arxiv_id":"1910.13461","paper":"/paper/bart-denoising-sequence-to-sequence-pre","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","date":"2019-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"i2r-simmc/i2r-simmc-2020","path":"src/util.py","file_url":"https://github.com/i2r-simmc/i2r-simmc-2020/blob/HEAD/src/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52e211fe8bfc51fe","mcp_get_code":{"code_sha256":"52e211fe8bfc51fe"}},{"arxiv_id":"1910.05744","paper":"/paper/powering-hidden-markov-model-by-neural","title":"Powering Hidden Markov Model by Neural Network based Generative Models","date":"2019-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FirstHandScientist/genhmm","path":"src/genHMM.py","file_url":"https://github.com/FirstHandScientist/genhmm/blob/HEAD/src/genHMM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8c0829cd95933fd9","mcp_get_code":{"code_sha256":"8c0829cd95933fd9"}},{"arxiv_id":"1909.09347","paper":"/paper/mimii-dataset-sound-dataset-for","title":"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Huanzhuo/IA-Net","path":"train_ids.py","file_url":"https://github.com/Huanzhuo/IA-Net/blob/HEAD/train_ids.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"02cfef883bae345a","mcp_get_code":{"code_sha256":"02cfef883bae345a"}},{"arxiv_id":"1908.09355","paper":"/paper/patient-knowledge-distillation-for-bert-model","title":"Patient Knowledge Distillation for BERT Model Compression","date":"2019-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Daniel-H-99/Patient-Knowledge-Distillation","path":"src/utils.py","file_url":"https://github.com/Daniel-H-99/Patient-Knowledge-Distillation/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"9e42bf8972501658","mcp_get_code":{"code_sha256":"9e42bf8972501658"}},{"arxiv_id":"1908.01207","paper":"/paper/predicting-dynamic-embedding-trajectory-in","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","date":"2019-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"srijankr/jodie","path":"library_models.py","file_url":"https://github.com/srijankr/jodie/blob/HEAD/library_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52baf54f8e02b11c","mcp_get_code":{"code_sha256":"52baf54f8e02b11c"}},{"arxiv_id":"1907.13124","paper":"/paper/impact-of-adversarial-examples-on-deep","title":"Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation","date":"2019-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"utkuozbulak/adaptive-segmentation-mask-attack","path":"src/helper_functions.py","file_url":"https://github.com/utkuozbulak/adaptive-segmentation-mask-attack/blob/HEAD/src/helper_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"34434771d4da85e1","mcp_get_code":{"code_sha256":"34434771d4da85e1"}},{"arxiv_id":"1907.12865","paper":"/paper/open-set-domain-adaptation-for-image-and","title":"Open Set Domain Adaptation for Image and Action Recognition","date":"2019-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Heliot7/open-set-da","path":"classifiers/SVM/liblinear-multicore-2.11-1/python/liblinearutil.py","file_url":"https://github.com/Heliot7/open-set-da/blob/HEAD/classifiers/SVM/liblinear-multicore-2.11-1/python/liblinearutil.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fb090c99a81bf999","mcp_get_code":{"code_sha256":"fb090c99a81bf999"}},{"arxiv_id":"1907.07034","paper":"/paper/uncertainty-aware-self-ensembling-model-for","title":"Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation","date":"2019-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bx0903/pdc","path":"code/utils/util.py","file_url":"https://github.com/bx0903/pdc/blob/HEAD/code/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"1906.06558","paper":"/paper/mask-based-unsupervised-content-transfer","title":"Mask Based Unsupervised Content Transfer","date":"2019-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rmokady/mbu-content-tansfer","path":"mask_utils.py","file_url":"https://github.com/rmokady/mbu-content-tansfer/blob/HEAD/mask_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6535fb7258fcf4e","mcp_get_code":{"code_sha256":"b6535fb7258fcf4e"}},{"arxiv_id":"1905.11736","paper":"/paper/cross-domain-transferability-of-adversarial","title":"Cross-Domain Transferability of Adversarial Perturbations","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Muzammal-Naseer/Cross-domain-perturbations","path":"utils.py","file_url":"https://github.com/Muzammal-Naseer/Cross-domain-perturbations/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b9a7bf60fbf85d8","mcp_get_code":{"code_sha256":"9b9a7bf60fbf85d8"}},{"arxiv_id":"1905.11092","paper":"/paper/a-rate-distortion-framework-for-explaining","title":"A Rate-Distortion Framework for Explaining Neural Network Decisions","date":"2019-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zib-iol/fw-rde","path":"mnist/models.py","file_url":"https://github.com/zib-iol/fw-rde/blob/HEAD/mnist/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a58965322bdce05","mcp_get_code":{"code_sha256":"1a58965322bdce05"}},{"arxiv_id":"1905.11092","paper":"/paper/a-rate-distortion-framework-for-explaining","title":"A Rate-Distortion Framework for Explaining Neural Network Decisions","date":"2019-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zib-iol/fw-rde","path":"stl10/models.py","file_url":"https://github.com/zib-iol/fw-rde/blob/HEAD/stl10/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84c10934bc2de02c","mcp_get_code":{"code_sha256":"84c10934bc2de02c"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neelabh17/MAVI-Face","path":"test_widerface.py","file_url":"https://github.com/neelabh17/MAVI-Face/blob/HEAD/test_widerface.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec9dcc3bf7178b3c","mcp_get_code":{"code_sha256":"ec9dcc3bf7178b3c"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"discipleofhamilton/RetinaFace","path":"convert_to_onnx.py","file_url":"https://github.com/discipleofhamilton/RetinaFace/blob/HEAD/convert_to_onnx.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"78859d5a5d2ba47b","mcp_get_code":{"code_sha256":"78859d5a5d2ba47b"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiaoxiu/facenetRetinaFace","path":"face/infer2.py","file_url":"https://github.com/qiaoxiu/facenetRetinaFace/blob/HEAD/face/infer2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a72a792028a0318","mcp_get_code":{"code_sha256":"4a72a792028a0318"}},{"arxiv_id":"1904.09675","paper":"/paper/bertscore-evaluating-text-generation-with","title":"BERTScore: Evaluating Text Generation with BERT","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lovit/ko-BERTScore","path":"KoBERTScore/score.py","file_url":"https://github.com/lovit/ko-BERTScore/blob/HEAD/KoBERTScore/score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3647b7dd8c5ac0d7","mcp_get_code":{"code_sha256":"3647b7dd8c5ac0d7"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ThanhDinhDat/center_net_points","path":"src/lib/models/model.py","file_url":"https://github.com/ThanhDinhDat/center_net_points/blob/HEAD/src/lib/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9bf1259e24d7137a","mcp_get_code":{"code_sha256":"9bf1259e24d7137a"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wastoon/XinTong_CenterNet","path":"src/lib/models/model.py","file_url":"https://github.com/Wastoon/XinTong_CenterNet/blob/HEAD/src/lib/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42af9659af852ebd","mcp_get_code":{"code_sha256":"42af9659af852ebd"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Guanghan/CenterNet-Gluon","path":"models/model.py","file_url":"https://github.com/Guanghan/CenterNet-Gluon/blob/HEAD/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd225652b42085b5","mcp_get_code":{"code_sha256":"bd225652b42085b5"}},{"arxiv_id":"1904.01774","paper":"/paper/image-generation-from-small-datasets-via","title":"Image Generation From Small Datasets via Batch Statistics Adaptation","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nogu-atsu/small-dataset-image-generation","path":"source/yaml_utils.py","file_url":"https://github.com/nogu-atsu/small-dataset-image-generation/blob/HEAD/source/yaml_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"619e0c5cf74a6c9f","mcp_get_code":{"code_sha256":"619e0c5cf74a6c9f"}},{"arxiv_id":"1903.07740","paper":"/paper/learning-to-augment-synthetic-images-for","title":"Learning to Augment Synthetic Images for Sim2Real Policy Transfer","date":"2019-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rstrudel/rlbc","path":"bc/model/utils.py","file_url":"https://github.com/rstrudel/rlbc/blob/HEAD/bc/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c32951a6416e3e1","mcp_get_code":{"code_sha256":"9c32951a6416e3e1"}},{"arxiv_id":"1902.08858","paper":"/paper/rethinking-action-spaces-for-reinforcement","title":"Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models","date":"2019-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snakeztc/NeuralDialog-LaRL","path":"FB/utils.py","file_url":"https://github.com/snakeztc/NeuralDialog-LaRL/blob/HEAD/FB/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7f7e4b0b6f9f8f19","mcp_get_code":{"code_sha256":"7f7e4b0b6f9f8f19"}},{"arxiv_id":"1901.08149","paper":"/paper/transfertransfo-a-transfer-learning-approach","title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","date":"2019-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cerebroai/AskIt","path":"model.py","file_url":"https://github.com/cerebroai/AskIt/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f849c7661306814","mcp_get_code":{"code_sha256":"2f849c7661306814"}},{"arxiv_id":"1812.00899","paper":"/paper/toward-scalable-neural-dialogue-state","title":"Toward Scalable Neural Dialogue State Tracking Model","date":"2018-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elnaaz/GCE-Model","path":"utils.py","file_url":"https://github.com/elnaaz/GCE-Model/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"code_sha256_prefix":"a87c666aeb87d9c4","mcp_get_code":{"code_sha256":"a87c666aeb87d9c4"}},{"arxiv_id":"1812.00101","paper":"/paper/dvc-an-end-to-end-deep-video-compression","title":"DVC: An End-to-end Deep Video Compression Framework","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"binzzheng/DVC-PyTorch","path":"net_dvc.py","file_url":"https://github.com/binzzheng/DVC-PyTorch/blob/HEAD/net_dvc.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"84650d27bbd7dcde","mcp_get_code":{"code_sha256":"84650d27bbd7dcde"}},{"arxiv_id":"1811.12823","paper":"/paper/molecular-sets-moses-a-benchmarking-platform","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","date":"2018-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"molecularsets/moses","path":"moses/latentgan/model.py","file_url":"https://github.com/molecularsets/moses/blob/HEAD/moses/latentgan/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa7749c022b18147","mcp_get_code":{"code_sha256":"aa7749c022b18147"}},{"arxiv_id":"1811.05090","paper":"/paper/a-general-method-for-amortizing-variational","title":"A General Method for Amortizing Variational Filtering","date":"2018-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joelouismarino/amortized-variational-filtering","path":"lib/models/load_model.py","file_url":"https://github.com/joelouismarino/amortized-variational-filtering/blob/HEAD/lib/models/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"827236cd04222fef","mcp_get_code":{"code_sha256":"827236cd04222fef"}},{"arxiv_id":"1811.01063","paper":"/paper/augmenting-neural-response-generation-with","title":"Augmenting Neural Response Generation with Context-Aware Topical Attention","date":"2018-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nouhadziri/THRED","path":"thred/models/model_helper.py","file_url":"https://github.com/nouhadziri/THRED/blob/HEAD/thred/models/model_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86296f212902a365","mcp_get_code":{"code_sha256":"86296f212902a365"}},{"arxiv_id":"1810.12241","paper":"/paper/few-shot-3d-multi-modal-medical-image","title":"Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arnab39/FewShot_GAN-Unet3D","path":"tensorflow/lib/utils.py","file_url":"https://github.com/arnab39/FewShot_GAN-Unet3D/blob/HEAD/tensorflow/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e4080fa699e175f","mcp_get_code":{"code_sha256":"3e4080fa699e175f"}},{"arxiv_id":"1810.08272","paper":"/paper/babyai-first-steps-towards-grounded-language","title":"BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning","date":"2018-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MathijsMul/babyai-emergent-guidance","path":"babyai/utils/model.py","file_url":"https://github.com/MathijsMul/babyai-emergent-guidance/blob/HEAD/babyai/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"41c6e1dc6b3c366b","mcp_get_code":{"code_sha256":"41c6e1dc6b3c366b"}},{"arxiv_id":"1810.08272","paper":"/paper/babyai-first-steps-towards-grounded-language","title":"BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning","date":"2018-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taungerine/babyai","path":"babyai/utils/model.py","file_url":"https://github.com/taungerine/babyai/blob/HEAD/babyai/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"b90f482c92958dce","mcp_get_code":{"code_sha256":"b90f482c92958dce"}},{"arxiv_id":"1810.01483","paper":"/paper/deepcmb-lensing-reconstruction-of-the-cosmic","title":"DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks","date":"2018-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EEmGuzman/resunet-cmb","path":"resunet/utils.py","file_url":"https://github.com/EEmGuzman/resunet-cmb/blob/HEAD/resunet/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8fdbdb7420d408d5","mcp_get_code":{"code_sha256":"8fdbdb7420d408d5"}},{"arxiv_id":"1807.06906","paper":"/paper/towards-automated-deep-learning-efficient","title":"Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search","date":"2018-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arberzela/EfficientNAS","path":"workers/arch_space/model/utils.py","file_url":"https://github.com/arberzela/EfficientNAS/blob/HEAD/workers/arch_space/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d828fe6876ca612","mcp_get_code":{"code_sha256":"2d828fe6876ca612"}},{"arxiv_id":"1807.03100","paper":"/paper/robust-text-to-sql-generation-with-execution","title":"Robust Text-to-SQL Generation with Execution-Guided Decoding","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/PointerSQL","path":"model/learn.py","file_url":"https://github.com/Microsoft/PointerSQL/blob/HEAD/model/learn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"270c014771199c5e","mcp_get_code":{"code_sha256":"270c014771199c5e"}},{"arxiv_id":"1806.03185","paper":"/paper/wave-u-net-a-multi-scale-neural-network-for","title":"Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation","date":"2018-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"f90/Wave-U-Net-Pytorch","path":"model/utils.py","file_url":"https://github.com/f90/Wave-U-Net-Pytorch/blob/HEAD/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"abf55a235181f2e0","mcp_get_code":{"code_sha256":"abf55a235181f2e0"}},{"arxiv_id":"1805.09655","paper":"/paper/global-locally-self-attentive-dialogue-state","title":"Global-Locally Self-Attentive Dialogue State Tracker","date":"2018-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/glad","path":"utils.py","file_url":"https://github.com/salesforce/glad/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"a87c666aeb87d9c4","mcp_get_code":{"code_sha256":"a87c666aeb87d9c4"}},{"arxiv_id":"1805.09655","paper":"/paper/global-locally-self-attentive-dialogue-state","title":"Global-Locally Self-Attentive Dialogue State Tracker","date":"2018-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS","path":"utils.py","file_url":"https://github.com/kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"code_sha256_prefix":"556de295149cb83e","mcp_get_code":{"code_sha256":"556de295149cb83e"}},{"arxiv_id":"1805.07594","paper":"/paper/generalizing-point-embeddings-using-the","title":"Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions","date":"2018-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albpurpura/PE4IR","path":"MP_WN_WE/util.py","file_url":"https://github.com/albpurpura/PE4IR/blob/HEAD/MP_WN_WE/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1bc245f33b630412","mcp_get_code":{"code_sha256":"1bc245f33b630412"}},{"arxiv_id":"1804.05685","paper":"/paper/a-discourse-aware-attention-model-for","title":"A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents","date":"2018-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlexGidiotis/DANCER-summ","path":"src/loaders.py","file_url":"https://github.com/AlexGidiotis/DANCER-summ/blob/HEAD/src/loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3b6c3e04b98ed4","mcp_get_code":{"code_sha256":"6a3b6c3e04b98ed4"}},{"arxiv_id":"1803.10081","paper":"/paper/deepjdot-deep-joint-distribution-optimal","title":"DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation","date":"2018-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbdamodaran/deepJDOT","path":"dnn.py","file_url":"https://github.com/bbdamodaran/deepJDOT/blob/HEAD/dnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a918c555112e13fa","mcp_get_code":{"code_sha256":"a918c555112e13fa"}},{"arxiv_id":"1803.09196","paper":"/paper/learning-type-aware-embeddings-for-fashion","title":"Learning Type-Aware Embeddings for Fashion Compatibility","date":"2018-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"owj0421/DeepFashion","path":"src/models/load.py","file_url":"https://github.com/owj0421/DeepFashion/blob/HEAD/src/models/load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a0f17a05cbef8b55","mcp_get_code":{"code_sha256":"a0f17a05cbef8b55"}},{"arxiv_id":"1803.09017","paper":"/paper/style-tokens-unsupervised-style-modeling","title":"Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KinglittleQ/GST-Tacotron","path":"generate.py","file_url":"https://github.com/KinglittleQ/GST-Tacotron/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3e8204b867d34ac","mcp_get_code":{"code_sha256":"d3e8204b867d34ac"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thomashirtz/soft-actor-critic","path":"soft_actor_critic/agent.py","file_url":"https://github.com/thomashirtz/soft-actor-critic/blob/HEAD/soft_actor_critic/agent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"706459e26fbb0b4c","mcp_get_code":{"code_sha256":"706459e26fbb0b4c"}},{"arxiv_id":"1711.05225","paper":"/paper/chexnet-radiologist-level-pneumonia-detection","title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning","date":"2017-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dstrick17/Deep-Learning-Project","path":"XRay_app/utils/model_utils.py","file_url":"https://github.com/dstrick17/Deep-Learning-Project/blob/HEAD/XRay_app/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ae48b7e9a8d2e9b","mcp_get_code":{"code_sha256":"7ae48b7e9a8d2e9b"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"enhuiz/torchnmt","path":"torchnmt/networks/transformer.py","file_url":"https://github.com/enhuiz/torchnmt/blob/HEAD/torchnmt/networks/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24b1c14ad199cff4","mcp_get_code":{"code_sha256":"24b1c14ad199cff4"}},{"arxiv_id":"1703.01789","paper":"/paper/sample-level-deep-convolutional-neural","title":"Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms","date":"2017-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kyungyunlee/sampleCNN-pytorch","path":"eval_tags.py","file_url":"https://github.com/kyungyunlee/sampleCNN-pytorch/blob/HEAD/eval_tags.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c185efb2cba25327","mcp_get_code":{"code_sha256":"c185efb2cba25327"}},{"arxiv_id":"1611.01704","paper":"/paper/end-to-end-optimized-image-compression","title":"End-to-end Optimized Image Compression","date":"2016-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujiaheng/iclr_17_compression","path":"model.py","file_url":"https://github.com/liujiaheng/iclr_17_compression/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"67b83d1f47e01c3f","mcp_get_code":{"code_sha256":"67b83d1f47e01c3f"}},{"arxiv_id":"1608.07017","paper":"/paper/ambient-sound-provides-supervision-for-visual","title":"Ambient Sound Provides Supervision for Visual Learning","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rowhanm/ambient-sound-self-supervision","path":"downstream_evaluation/ft_util.py","file_url":"https://github.com/rowhanm/ambient-sound-self-supervision/blob/HEAD/downstream_evaluation/ft_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3abf2c16454d0592","mcp_get_code":{"code_sha256":"3abf2c16454d0592"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atapour/ransomware-classification","path":"model/network.py","file_url":"https://github.com/atapour/ransomware-classification/blob/HEAD/model/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5aff6523600f9f77","mcp_get_code":{"code_sha256":"5aff6523600f9f77"}},{"arxiv_id":"1607.04606","paper":"/paper/enriching-word-vectors-with-subword","title":"Enriching Word Vectors with Subword Information","date":"2016-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhang2010hao/cw2vec-pytorch","path":"cw2vec.py","file_url":"https://github.com/zhang2010hao/cw2vec-pytorch/blob/HEAD/cw2vec.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"847e1524ed1bff1a","mcp_get_code":{"code_sha256":"847e1524ed1bff1a"}},{"arxiv_id":"1606.05814","paper":"/paper/eye-tracking-for-everyone","title":"Eye Tracking for Everyone","date":"2016-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hugochan/Eye-Tracker","path":"validation_script.py","file_url":"https://github.com/hugochan/Eye-Tracker/blob/HEAD/validation_script.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f7a814ffd4d776cb","mcp_get_code":{"code_sha256":"f7a814ffd4d776cb"}},{"arxiv_id":"1603.00831","paper":"/paper/mot16-a-benchmark-for-multi-object-tracking","title":"MOT16: A Benchmark for Multi-Object Tracking","date":"2016-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toptrack/toptrack2023","path":"src/lib/models/model.py","file_url":"https://github.com/toptrack/toptrack2023/blob/HEAD/src/lib/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42af9659af852ebd","mcp_get_code":{"code_sha256":"42af9659af852ebd"}},{"arxiv_id":"1602.00763","paper":"/paper/simple-online-and-realtime-tracking","title":"Simple Online and Realtime Tracking","date":"2016-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nakul-shahdadpuri/narknet","path":"classify/image.py","file_url":"https://github.com/nakul-shahdadpuri/narknet/blob/HEAD/classify/image.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72f789555f7a818","mcp_get_code":{"code_sha256":"d72f789555f7a818"}},{"arxiv_id":"1511.05190","paper":"/paper/jet-images-deep-learning-edition","title":"Jet-Images -- Deep Learning Edition","date":"2015-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepjets/deepjets","path":"deepjets/models.py","file_url":"https://github.com/deepjets/deepjets/blob/HEAD/deepjets/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"040234d4633b444f","mcp_get_code":{"code_sha256":"040234d4633b444f"}},{"arxiv_id":"1205.2618","paper":"/paper/bpr-bayesian-personalized-ranking-from","title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","date":"2012-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EternalImmortal/bpr","path":"util_origin.py","file_url":"https://github.com/EternalImmortal/bpr/blob/HEAD/util_origin.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59ad70782b2fa0d4","mcp_get_code":{"code_sha256":"59ad70782b2fa0d4"}},{"arxiv_id":"openreview_g9G7qyAzki","paper":null,"title":"arXiv:openreview_g9G7qyAzki","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"The-Inscrutable-X/CalibratedModelAgnosticCorrectness","path":"utils/model_utils.py","file_url":"https://github.com/The-Inscrutable-X/CalibratedModelAgnosticCorrectness/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4a050a6fc851e47d","mcp_get_code":{"code_sha256":"4a050a6fc851e47d"}},{"arxiv_id":"openreview_YXEXsGHwpq","paper":null,"title":"arXiv:openreview_YXEXsGHwpq","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SakanaAI/sparser-faster-llms","path":"hydra_utils.py","file_url":"https://github.com/SakanaAI/sparser-faster-llms/blob/HEAD/hydra_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0cc6ed732525b97","mcp_get_code":{"code_sha256":"b0cc6ed732525b97"}},{"arxiv_id":"openreview_OdrEcfMogy","paper":null,"title":"arXiv:openreview_OdrEcfMogy","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YuanGao-YG/OneForecast","path":"inference.py","file_url":"https://github.com/YuanGao-YG/OneForecast/blob/HEAD/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5470c906a79f31ac","mcp_get_code":{"code_sha256":"5470c906a79f31ac"}},{"arxiv_id":"openreview_JjILY9i6Wi","paper":null,"title":"arXiv:openreview_JjILY9i6Wi","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nwlt/IPMark","path":"calc_ppl.py","file_url":"https://github.com/nwlt/IPMark/blob/HEAD/calc_ppl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"877a7adba26c71cc","mcp_get_code":{"code_sha256":"877a7adba26c71cc"}},{"arxiv_id":"ijcai2025_1059","paper":null,"title":"arXiv:ijcai2025_1059","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kracr/moral-decision-dataset","path":"code/MDD/run_def.py","file_url":"https://github.com/kracr/moral-decision-dataset/blob/HEAD/code/MDD/run_def.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"58cc3088ebab0601","mcp_get_code":{"code_sha256":"58cc3088ebab0601"}},{"arxiv_id":"ijcai2025_1059","paper":null,"title":"arXiv:ijcai2025_1059","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kracr/moral-decision-dataset","path":"code/MDD/run_def.py","file_url":"https://github.com/kracr/moral-decision-dataset/blob/HEAD/code/MDD/run_def.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c6b941beb75bbd69","mcp_get_code":{"code_sha256":"c6b941beb75bbd69"}},{"arxiv_id":"ijcai2022_0142","paper":null,"title":"arXiv:ijcai2022_0142","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KUXN98/ACHNet","path":"network/resnet.py","file_url":"https://github.com/KUXN98/ACHNet/blob/HEAD/network/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9aa9c091dca033fa","mcp_get_code":{"code_sha256":"9aa9c091dca033fa"}},{"arxiv_id":"aaai_29784","paper":null,"title":"arXiv:aaai_29784","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HITsz-TMG/Ext-Sub","path":"eval/toxic_eval_generation.py","file_url":"https://github.com/HITsz-TMG/Ext-Sub/blob/HEAD/eval/toxic_eval_generation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a1e619bcc580a63","mcp_get_code":{"code_sha256":"8a1e619bcc580a63"}},{"arxiv_id":"aaai_29778","paper":null,"title":"arXiv:aaai_29778","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NLPCode/LIFE","path":"models/filter_synthetic_data.py","file_url":"https://github.com/NLPCode/LIFE/blob/HEAD/models/filter_synthetic_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8bad70ec9e4243be","mcp_get_code":{"code_sha256":"8bad70ec9e4243be"}},{"arxiv_id":"aaai_27969","paper":null,"title":"arXiv:aaai_27969","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"TianyuGoGO/XPNG","path":"models/extract_fpn_with_ckpt_load_from_detectron2.py","file_url":"https://github.com/TianyuGoGO/XPNG/blob/HEAD/models/extract_fpn_with_ckpt_load_from_detectron2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"68c73cc8252a81c8","mcp_get_code":{"code_sha256":"68c73cc8252a81c8"}},{"arxiv_id":"aaai_25986","paper":null,"title":"arXiv:aaai_25986","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"joshua840/RobustAGA","path":"project/module/lrp_module/load_model.py","file_url":"https://github.com/joshua840/RobustAGA/blob/HEAD/project/module/lrp_module/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9b6883959de4a817","mcp_get_code":{"code_sha256":"9b6883959de4a817"}},{"arxiv_id":"aaai_16089","paper":null,"title":"arXiv:aaai_16089","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lipiji/neural-summ-cnndm-pytorch","path":"utils_pg.py","file_url":"https://github.com/lipiji/neural-summ-cnndm-pytorch/blob/HEAD/utils_pg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"56e39d938150e42d","mcp_get_code":{"code_sha256":"56e39d938150e42d"}},{"arxiv_id":"Zhuang_CMAD_Correlation-Aware_and_Modalities-Aware_Distillation_for_Multimodal_Sentiment_Analysis_with_ICCV_2025_paper","paper":null,"title":"arXiv:Zhuang_CMAD_Correlation-Aware_and_Modalities-Aware_Distillation_for_Multimodal_Sentiment_Analysis_with_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YetZzzzzz/CMAD","path":"CMAD_sentiment/Student_Model/utils.py","file_url":"https://github.com/YetZzzzzz/CMAD/blob/HEAD/CMAD_sentiment/Student_Model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9a924bee73f12e1","mcp_get_code":{"code_sha256":"f9a924bee73f12e1"}},{"arxiv_id":"Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"WYC-321/MCF","path":"code/utils/util.py","file_url":"https://github.com/WYC-321/MCF/blob/HEAD/code/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"Phoo_Coarsely-Labeled_Data_for_Better_Few-Shot_Transfer_ICCV_2021_paper","paper":null,"title":"arXiv:Phoo_Coarsely-Labeled_Data_for_Better_Few-Shot_Transfer_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cpphoo/PAS","path":"metaOptNet/evaluate_metaOptNet.py","file_url":"https://github.com/cpphoo/PAS/blob/HEAD/metaOptNet/evaluate_metaOptNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22a028193068582d","mcp_get_code":{"code_sha256":"22a028193068582d"}},{"arxiv_id":"Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper","paper":null,"title":"arXiv:Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"JuzhengMiao/CauSSL","path":"utils/util.py","file_url":"https://github.com/JuzhengMiao/CauSSL/blob/HEAD/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad6309e2c80e2b00","mcp_get_code":{"code_sha256":"ad6309e2c80e2b00"}},{"arxiv_id":"Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper","paper":null,"title":"arXiv:Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SensorsINI/e2p","path":"train/load_model.py","file_url":"https://github.com/SensorsINI/e2p/blob/HEAD/train/load_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"73a5ec57d5b8bc98","mcp_get_code":{"code_sha256":"73a5ec57d5b8bc98"}},{"arxiv_id":"2025.naacl-demo.13","paper":null,"title":"arXiv:2025.naacl-demo.13","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"IBM/InspectorRAGet","path":"converters/tau2bench/convert.py","file_url":"https://github.com/IBM/InspectorRAGet/blob/HEAD/converters/tau2bench/convert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f81c49c4fda4f53c","mcp_get_code":{"code_sha256":"f81c49c4fda4f53c"}},{"arxiv_id":"2025.findings-emnlp.314","paper":null,"title":"arXiv:2025.findings-emnlp.314","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Syuchin/MARS-Bench","path":"chat/models/openai_api.py","file_url":"https://github.com/Syuchin/MARS-Bench/blob/HEAD/chat/models/openai_api.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3ef8133b14b20128","mcp_get_code":{"code_sha256":"3ef8133b14b20128"}},{"arxiv_id":"2025.acl-long.851","paper":null,"title":"arXiv:2025.acl-long.851","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"uw-nsl/SafeDecoding","path":"utils/model.py","file_url":"https://github.com/uw-nsl/SafeDecoding/blob/HEAD/utils/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ff17004f6e9124f","mcp_get_code":{"code_sha256":"3ff17004f6e9124f"}},{"arxiv_id":"2025.acl-demo.47","paper":null,"title":"arXiv:2025.acl-demo.47","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SjJ1017/CiteLab","path":"citekit/cite_modules/LLM.py","file_url":"https://github.com/SjJ1017/CiteLab/blob/HEAD/citekit/cite_modules/LLM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b1aa6c33e7b0d55","mcp_get_code":{"code_sha256":"2b1aa6c33e7b0d55"}},{"arxiv_id":"2024.findings-acl.975","paper":null,"title":"arXiv:2024.findings-acl.975","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CLU-UML/MedDec","path":"model.py","file_url":"https://github.com/CLU-UML/MedDec/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ed5bd24255e6fe3","mcp_get_code":{"code_sha256":"4ed5bd24255e6fe3"}},{"arxiv_id":"2024.findings-acl.290","paper":null,"title":"arXiv:2024.findings-acl.290","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ChnQ/TracingLLM","path":"src/generate_activations.py","file_url":"https://github.com/ChnQ/TracingLLM/blob/HEAD/src/generate_activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e1c7e2781771e313","mcp_get_code":{"code_sha256":"e1c7e2781771e313"}},{"arxiv_id":"2024.emnlp-main.758","paper":null,"title":"arXiv:2024.emnlp-main.758","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HuangOwen/CoT-Influx","path":"example_retrieval_pruner.py","file_url":"https://github.com/HuangOwen/CoT-Influx/blob/HEAD/example_retrieval_pruner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae6cc5127a6d387e","mcp_get_code":{"code_sha256":"ae6cc5127a6d387e"}},{"arxiv_id":"2023.findings-acl.611","paper":null,"title":"arXiv:2023.findings-acl.611","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/RobTest","path":"src/robtest.py","file_url":"https://github.com/thunlp/RobTest/blob/HEAD/src/robtest.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ec92012e6d39c59","mcp_get_code":{"code_sha256":"7ec92012e6d39c59"}},{"arxiv_id":"2023.emnlp-industry.4","paper":null,"title":"arXiv:2023.emnlp-industry.4","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shamilcm/CTranslate2","path":"python/ctranslate2/converters/opennmt_tf.py","file_url":"https://github.com/shamilcm/CTranslate2/blob/HEAD/python/ctranslate2/converters/opennmt_tf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e437d426d2d2a16d","mcp_get_code":{"code_sha256":"e437d426d2d2a16d"}},{"arxiv_id":"2023.acl-long.119","paper":null,"title":"arXiv:2023.acl-long.119","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lsvih/AtTGen","path":"evaluation.py","file_url":"https://github.com/lsvih/AtTGen/blob/HEAD/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d10712c7114d35a8","mcp_get_code":{"code_sha256":"d10712c7114d35a8"}},{"arxiv_id":"2022.findings-emnlp.174","paper":null,"title":"arXiv:2022.findings-emnlp.174","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"subercui/CodeExp","path":"code2text/GPT-J/eval-gpt-neo.py","file_url":"https://github.com/subercui/CodeExp/blob/HEAD/code2text/GPT-J/eval-gpt-neo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b57f640e534abe05","mcp_get_code":{"code_sha256":"b57f640e534abe05"}},{"arxiv_id":"2022.findings-emnlp.174","paper":null,"title":"arXiv:2022.findings-emnlp.174","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"subercui/CodeExp","path":"code2text/codeT5/eval-codeT5.py","file_url":"https://github.com/subercui/CodeExp/blob/HEAD/code2text/codeT5/eval-codeT5.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9235f818b8e6af4d","mcp_get_code":{"code_sha256":"9235f818b8e6af4d"}},{"arxiv_id":"2022.findings-aacl.4","paper":null,"title":"arXiv:2022.findings-aacl.4","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"anthonysicilia/LEATHER-AACL2022","path":"utils/model_loading.py","file_url":"https://github.com/anthonysicilia/LEATHER-AACL2022/blob/HEAD/utils/model_loading.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d6d70d78329934e","mcp_get_code":{"code_sha256":"7d6d70d78329934e"}},{"arxiv_id":"2020.emnlp-main.315","paper":null,"title":"arXiv:2020.emnlp-main.315","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mrpeerat/SEFR_CUT","path":"sefr_cut/deepcut/deepcut.py","file_url":"https://github.com/mrpeerat/SEFR_CUT/blob/HEAD/sefr_cut/deepcut/deepcut.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f55545359788d01","mcp_get_code":{"code_sha256":"2f55545359788d01"}},{"arxiv_id":"136720506","paper":null,"title":"arXiv:136720506","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YuchenLiu98/ECCV2022-SDA-FAS","path":"SDA-FAS/experiment/I_C_M_to_O/train_SDAFAS.py","file_url":"https://github.com/YuchenLiu98/ECCV2022-SDA-FAS/blob/HEAD/SDA-FAS/experiment/I_C_M_to_O/train_SDAFAS.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b1a24aa7eff9e84","mcp_get_code":{"code_sha256":"7b1a24aa7eff9e84"}}]}