{"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-yaml","entry":"load_yaml","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":107,"n_papers_ran":47,"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":77,"n_samples_ran":33,"n_samples_fingerprinted":0,"n_places":111,"n_places_pointer_only":38,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":14,"ran_fixture":0,"ran":19,"unverified":44},"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":"2609.15571","paper":"/paper/arxiv-2609-15571","title":"GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"gzhu-hcai/Med-Unlearn","path":"pipeline/step_4_generate_original_and_naive_model_specs.py","file_url":"https://github.com/gzhu-hcai/Med-Unlearn/blob/HEAD/pipeline/step_4_generate_original_and_naive_model_specs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"27618803acf80c3a","mcp_get_code":{"code_sha256":"27618803acf80c3a"}},{"arxiv_id":"2609.13237","paper":"/paper/arxiv-2609-13237","title":"Occlusal Geometry in Closed Form for Orthodontic Report Generation","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"GIND123/ODIN_toothfairy4","path":"src/bite2text/config.py","file_url":"https://github.com/GIND123/ODIN_toothfairy4/blob/HEAD/src/bite2text/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"068144f98f975540","mcp_get_code":{"code_sha256":"068144f98f975540"}},{"arxiv_id":"2609.01814","paper":"/paper/arxiv-2609-01814","title":"When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"yoheinakajima/distributed-discovery","path":"src/distributed_discovery/agent_ops/core.py","file_url":"https://github.com/yoheinakajima/distributed-discovery/blob/HEAD/src/distributed_discovery/agent_ops/core.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7839be7041db51a2","mcp_get_code":{"code_sha256":"7839be7041db51a2"}},{"arxiv_id":"2608.12149","paper":"/paper/arxiv-2608-12149","title":"Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateaus","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"StartLuxLabs/Massive-Activations-HLA","path":"src/massive_activations_hla/config.py","file_url":"https://github.com/StartLuxLabs/Massive-Activations-HLA/blob/HEAD/src/massive_activations_hla/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01de090b986f48d3","mcp_get_code":{"code_sha256":"01de090b986f48d3"}},{"arxiv_id":"2606.19827","paper":"/paper/arxiv-2606-19827","title":"When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"labhai/Adaptive-Binning","path":"adaptive_binning/config.py","file_url":"https://github.com/labhai/Adaptive-Binning/blob/HEAD/adaptive_binning/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbea842f0ce17acd","mcp_get_code":{"code_sha256":"dbea842f0ce17acd"}},{"arxiv_id":"2606.03066","paper":"/paper/arxiv-2606-03066","title":"CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"shen8424/CORE","path":"code/train_stage_pipeline.py","file_url":"https://github.com/shen8424/CORE/blob/HEAD/code/train_stage_pipeline.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"83396ec95592fb23","mcp_get_code":{"code_sha256":"83396ec95592fb23"}},{"arxiv_id":"2605.24144","paper":"/paper/arxiv-2605-24144","title":"EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"dbw6/Eva","path":"simulator/config.py","file_url":"https://github.com/dbw6/Eva/blob/HEAD/simulator/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"170f525d725f54b6","mcp_get_code":{"code_sha256":"170f525d725f54b6"}},{"arxiv_id":"2605.21076","paper":"/paper/arxiv-2605-21076","title":"GradeLegal: Automated Grading for German Legal Cases *","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"abdullahalzubaer/icail2026","path":"code/inference/litellm_inf.py","file_url":"https://github.com/abdullahalzubaer/icail2026/blob/HEAD/code/inference/litellm_inf.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"83396ec95592fb23","mcp_get_code":{"code_sha256":"83396ec95592fb23"}},{"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/core/config_loader.py","file_url":"https://github.com/StellarLuminosity/Energy/blob/HEAD/distill_bench/core/config_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5493aa802657f082","mcp_get_code":{"code_sha256":"5493aa802657f082"}},{"arxiv_id":"2604.16919","paper":"/paper/arxiv-2604-16919","title":"Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"NA-HMC/NA-HMC","path":"ldm_loader.py","file_url":"https://github.com/NA-HMC/NA-HMC/blob/HEAD/ldm_loader.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"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":"experiments/run_benchmark_final_clean.py","file_url":"https://github.com/mkboch/UDAE/blob/HEAD/experiments/run_benchmark_final_clean.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"09b3ca5c2d7d5b1d","mcp_get_code":{"code_sha256":"09b3ca5c2d7d5b1d"}},{"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":"experiments/run_experiment.py","file_url":"https://github.com/mkboch/UDAE/blob/HEAD/experiments/run_experiment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"32b3e0f038f60081","mcp_get_code":{"code_sha256":"32b3e0f038f60081"}},{"arxiv_id":"2602.11882","paper":"/paper/arxiv-2602-11882","title":"Where Bits Matter in World Model Planning: A Paired Mixed-Bit Study for Efficient Spatial Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"suraj-ranganath/DINO-MBQuant","path":"experiments/dino_runner.py","file_url":"https://github.com/suraj-ranganath/DINO-MBQuant/blob/HEAD/experiments/dino_runner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"305acb4c182e7a53","mcp_get_code":{"code_sha256":"305acb4c182e7a53"}},{"arxiv_id":"2602.11882","paper":"/paper/arxiv-2602-11882","title":"Where Bits Matter in World Model Planning: A Paired Mixed-Bit Study for Efficient Spatial Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"suraj-ranganath/DINO-MBQuant","path":"experiments/benchmark_predictor_coreml.py","file_url":"https://github.com/suraj-ranganath/DINO-MBQuant/blob/HEAD/experiments/benchmark_predictor_coreml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1aec678628566476","mcp_get_code":{"code_sha256":"1aec678628566476"}},{"arxiv_id":"2601.22420","paper":"/paper/arxiv-2601-22420","title":"METALEAD: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"RoelTim/metalead","path":"src/extract_tuples.py","file_url":"https://github.com/RoelTim/metalead/blob/HEAD/src/extract_tuples.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2c8120a1ffc2d1cd","mcp_get_code":{"code_sha256":"2c8120a1ffc2d1cd"}},{"arxiv_id":"2601.21159","paper":"/paper/arxiv-2601-21159","title":"Spatial-Regularization-Aware Dual-Branch Collaborative Inference for Training-Free OVSS in Remote Sensing Imagery","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yu-ni1989/SDCI","path":"util/tools.py","file_url":"https://github.com/yu-ni1989/SDCI/blob/HEAD/util/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ecf19d7f6016dce","mcp_get_code":{"code_sha256":"7ecf19d7f6016dce"}},{"arxiv_id":"2601.15771","paper":"/paper/arxiv-2601-15771","title":"Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"SZU-ADDG/GenRel-DDI","path":"model.py","file_url":"https://github.com/SZU-ADDG/GenRel-DDI/blob/HEAD/model.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":"ab15b585260d2f31","mcp_get_code":{"code_sha256":"ab15b585260d2f31"}},{"arxiv_id":"2601.05654","paper":"/paper/arxiv-2601-05654","title":"Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"holi-lab/ReCAP","path":"recap/profiler/profiler.py","file_url":"https://github.com/holi-lab/ReCAP/blob/HEAD/recap/profiler/profiler.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":"b4e41872f59b5cab","mcp_get_code":{"code_sha256":"b4e41872f59b5cab"}},{"arxiv_id":"2601.05637","paper":"/paper/arxiv-2601-05637","title":"GenCtrl -A Formal Controllability Toolkit for Generative Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"apple/ml-genctrl","path":"genctrl/utils/setup_utils.py","file_url":"https://github.com/apple/ml-genctrl/blob/HEAD/genctrl/utils/setup_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7b5610878e29ab06","mcp_get_code":{"code_sha256":"7b5610878e29ab06"}},{"arxiv_id":"2601.01407","paper":"/paper/arxiv-2601-01407","title":"From Emotion Classification to Emotional Reasoning: Enhancing Emotional Intelligence in Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"kernelism/EC2ER","path":"benchmarking/src/utils.py","file_url":"https://github.com/kernelism/EC2ER/blob/HEAD/benchmarking/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ee00ac0a8a462b63","mcp_get_code":{"code_sha256":"ee00ac0a8a462b63"}},{"arxiv_id":"2511.00117","paper":"/paper/arxiv-2511-00117","title":"DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"83396ec95592fb23","mcp_get_code":{"code_sha256":"83396ec95592fb23"}},{"arxiv_id":"2510.18204","paper":"/paper/arxiv-2510-18204","title":"RESCUE: Retrieval Augmented Secure Code Generation","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"steven1518/RESCUE","path":"src/common/utils.py","file_url":"https://github.com/steven1518/RESCUE/blob/HEAD/src/common/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4931b9a8d32ce699","mcp_get_code":{"code_sha256":"4931b9a8d32ce699"}},{"arxiv_id":"2509.19003","paper":"/paper/arxiv-2509-19003","title":"Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"baaivision/CoS","path":"eval/mmmu/data_utils.py","file_url":"https://github.com/baaivision/CoS/blob/HEAD/eval/mmmu/data_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":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2509.12158","paper":"/paper/arxiv-2509-12158","title":"Pun Unintended: LLMs and the Illusion of Humor Understanding","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"alezanga/punintended","path":"utils/io.py","file_url":"https://github.com/alezanga/punintended/blob/HEAD/utils/io.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2c3bdcb7c41bafe0","mcp_get_code":{"code_sha256":"2c3bdcb7c41bafe0"}},{"arxiv_id":"2507.04127","paper":null,"title":"arXiv:2507.04127","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"awslabs/graphrag-toolkit","path":"byokg-rag/src/graphrag_toolkit/byokg_rag/byokg_query_engine.py","file_url":"https://github.com/awslabs/graphrag-toolkit/blob/HEAD/byokg-rag/src/graphrag_toolkit/byokg_rag/byokg_query_engine.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a73dde4f787afe07","mcp_get_code":{"code_sha256":"a73dde4f787afe07"}},{"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/finetune.py","file_url":"https://github.com/mehrdadsaberi/msa_unlearning/blob/HEAD/src/finetune.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"83396ec95592fb23","mcp_get_code":{"code_sha256":"83396ec95592fb23"}},{"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":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fd8d7039bbcb386b","mcp_get_code":{"code_sha256":"fd8d7039bbcb386b"}},{"arxiv_id":"2505.11557","paper":"/paper/ac-lora-almost-training-free-access-control","title":"AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs","date":"2025-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huawei-csl/AC-LoRA","path":"src/configuration/utils.py","file_url":"https://github.com/huawei-csl/AC-LoRA/blob/HEAD/src/configuration/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"43fc1e7af0a7d755","mcp_get_code":{"code_sha256":"43fc1e7af0a7d755"}},{"arxiv_id":"2505.07215","paper":"/paper/measuring-general-intelligence-with-generated","title":"Measuring General Intelligence with Generated Games","date":"2025-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vivek3141/gg-bench","path":"gg_bench/utils/load_yaml.py","file_url":"https://github.com/vivek3141/gg-bench/blob/HEAD/gg_bench/utils/load_yaml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44ae92767e1cb329","mcp_get_code":{"code_sha256":"44ae92767e1cb329"}},{"arxiv_id":"2503.12559","paper":"/paper/adaretake-adaptive-redundancy-reduction-to","title":"AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding","date":"2025-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sczwangxiao/video-flexreduc","path":"retake/infer_eval.py","file_url":"https://github.com/sczwangxiao/video-flexreduc/blob/HEAD/retake/infer_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d37f4e5bd2ad847b","mcp_get_code":{"code_sha256":"d37f4e5bd2ad847b"}},{"arxiv_id":"2502.05352","paper":"/paper/itbench-evaluating-ai-agents-across-diverse","title":"ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks","date":"2025-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/itbench-sample-scenarios","path":"scenarios/ciso/4.upd-cis-b-k8s-kyverno/evaluation.py","file_url":"https://github.com/IBM/itbench-sample-scenarios/blob/HEAD/scenarios/ciso/4.upd-cis-b-k8s-kyverno/evaluation.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":"3190d9673df6bffb","mcp_get_code":{"code_sha256":"3190d9673df6bffb"}},{"arxiv_id":"2502.01189","paper":"/paper/compressed-image-generation-with-denoising","title":"Compressed Image Generation with Denoising Diffusion Codebook Models","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DDCM-2025/ddcm-compressed-image-generation","path":"compressed_blind_face_restoration.py","file_url":"https://github.com/DDCM-2025/ddcm-compressed-image-generation/blob/HEAD/compressed_blind_face_restoration.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2502.00698","paper":"/paper/mm-iq-benchmarking-human-like-abstraction-and-1","title":"MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models","date":"2025-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AceCHQ/MMIQ","path":"mmiq/utils/data_utils.py","file_url":"https://github.com/AceCHQ/MMIQ/blob/HEAD/mmiq/utils/data_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":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2501.11839","paper":"/paper/supervised-learning-for-analog-and-rf-circuit","title":"Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights","date":"2025-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"avestimehrresearchgroup/aicircuit","path":"Utils/utils.py","file_url":"https://github.com/avestimehrresearchgroup/aicircuit/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":"efa03cce29a7ed88","mcp_get_code":{"code_sha256":"efa03cce29a7ed88"}},{"arxiv_id":"2412.20504","paper":"/paper/retake-reducing-temporal-and-knowledge","title":"ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding","date":"2024-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sczwangxiao/video-retake","path":"retake/infer_eval.py","file_url":"https://github.com/sczwangxiao/video-retake/blob/HEAD/retake/infer_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d37f4e5bd2ad847b","mcp_get_code":{"code_sha256":"d37f4e5bd2ad847b"}},{"arxiv_id":"2411.02322","paper":"/paper/layerdag-a-layerwise-autoregressive-diffusion","title":"LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Graph-COM/LayerDAG","path":"setup_utils.py","file_url":"https://github.com/Graph-COM/LayerDAG/blob/HEAD/setup_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":"3a54c41960ac921e","mcp_get_code":{"code_sha256":"3a54c41960ac921e"}},{"arxiv_id":"2411.01573","paper":"/paper/conditional-controllable-image-fusion","title":"Conditional Controllable Image Fusion","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2411.00204","paper":"/paper/restor-knowledge-recovery-through-machine","title":"RESTOR: Knowledge Recovery through Machine Unlearning","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"83396ec95592fb23","mcp_get_code":{"code_sha256":"83396ec95592fb23"}},{"arxiv_id":"2411.00204","paper":"/paper/restor-knowledge-recovery-through-machine","title":"RESTOR: Knowledge Recovery through Machine Unlearning","date":"2024-10-31","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":"fd8d7039bbcb386b","mcp_get_code":{"code_sha256":"fd8d7039bbcb386b"}},{"arxiv_id":"2410.13907","paper":"/paper/nsmark-null-space-based-black-box","title":"NSmark: Null Space Based Black-box Watermarking Defense Framework for Language Models","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongdongzhaoup/nsmark","path":"configs/config.py","file_url":"https://github.com/dongdongzhaoup/nsmark/blob/HEAD/configs/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"27b08d7ef463db1c","mcp_get_code":{"code_sha256":"27b08d7ef463db1c"}},{"arxiv_id":"2410.08355","paper":"/paper/metalic-meta-learning-in-context-with-protein","title":"Metalic: Meta-Learning In-Context with Protein Language Models","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"instadeepai/metalic","path":"meta/io.py","file_url":"https://github.com/instadeepai/metalic/blob/HEAD/meta/io.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":"c0273d113af483cb","mcp_get_code":{"code_sha256":"c0273d113af483cb"}},{"arxiv_id":"2410.04479","paper":"/paper/sitcom-step-wise-triple-consistent-diffusion","title":"SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems","date":"2024-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2410.04360","paper":"/paper/gensim-a-general-social-simulation-platform","title":"GenSim: A General Social Simulation Platform with Large Language Model based Agents","date":"2024-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TangJiakai/GenSim","path":"simulation/helpers/utils.py","file_url":"https://github.com/TangJiakai/GenSim/blob/HEAD/simulation/helpers/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cfb2071ad5f20847","mcp_get_code":{"code_sha256":"cfb2071ad5f20847"}},{"arxiv_id":"2410.03463","paper":"/paper/diffusion-state-guided-projected-gradient-for","title":"Diffusion State-Guided Projected Gradient for Inverse Problems","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LituRout/PSLD","path":"diffusion-posterior-sampling/sample_condition.py","file_url":"https://github.com/LituRout/PSLD/blob/HEAD/diffusion-posterior-sampling/sample_condition.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2409.17266","paper":"/paper/aapm-large-language-model-agent-based-asset","title":"Empirical Asset Pricing with Large Language Model Agents","date":"2024-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chengjunyan1/AAPM","path":"utils.py","file_url":"https://github.com/chengjunyan1/AAPM/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":"ed4de14f882fac70","mcp_get_code":{"code_sha256":"ed4de14f882fac70"}},{"arxiv_id":"2409.17213","paper":"/paper/plurals-a-system-for-guiding-llms-via","title":"Plurals: A System for Guiding LLMs Via Simulated Social Ensembles","date":"2024-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"josh-ashkinaze/plurals","path":"plurals/helpers.py","file_url":"https://github.com/josh-ashkinaze/plurals/blob/HEAD/plurals/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7148444dd4b8e9bc","mcp_get_code":{"code_sha256":"7148444dd4b8e9bc"}},{"arxiv_id":"2409.07497","paper":"/paper/oneedit-a-neural-symbolic-collaboratively","title":"OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System","date":"2024-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/oneedit","path":"src/utils.py","file_url":"https://github.com/zjunlp/oneedit/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2920a1355ccabfb","mcp_get_code":{"code_sha256":"b2920a1355ccabfb"}},{"arxiv_id":"2408.10276","paper":"/paper/fedkim-adaptive-federated-knowledge-injection","title":"FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models","date":"2024-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiaochenWang-PSU/FedKIM","path":"FedKIM/src/ChEF/evaluator.py","file_url":"https://github.com/XiaochenWang-PSU/FedKIM/blob/HEAD/FedKIM/src/ChEF/evaluator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fb66043ff5364d4a","mcp_get_code":{"code_sha256":"fb66043ff5364d4a"}},{"arxiv_id":"2408.09227","paper":"/paper/fedmeki-a-benchmark-for-scaling-medical","title":"FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection","date":"2024-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"psudslab/FEDMEKI","path":"FedMEKI/src/ChEF/evaluator.py","file_url":"https://github.com/psudslab/FEDMEKI/blob/HEAD/FedMEKI/src/ChEF/evaluator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fb66043ff5364d4a","mcp_get_code":{"code_sha256":"fb66043ff5364d4a"}},{"arxiv_id":"2406.14852","paper":"/paper/is-a-picture-worth-a-thousand-words-delving","title":"Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BAAI-DCAI/Bunny","path":"bunny/eval/model_vqa_cmmmu.py","file_url":"https://github.com/BAAI-DCAI/Bunny/blob/HEAD/bunny/eval/model_vqa_cmmmu.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":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2406.13642","paper":"/paper/spatialbot-precise-spatial-understanding-with","title":"SpatialBot: Precise Spatial Understanding with Vision Language Models","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baai-dcai/spatialbot","path":"bunny/eval/eval_rtx.py","file_url":"https://github.com/baai-dcai/spatialbot/blob/HEAD/bunny/eval/eval_rtx.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2406.08249","paper":"/paper/dataset-enhancement-with-instance-level","title":"Dataset Enhancement with Instance-Level Augmentations","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KupynOrest/instance_augmentation","path":"instance_augmentation/generate_dataset.py","file_url":"https://github.com/KupynOrest/instance_augmentation/blob/HEAD/instance_augmentation/generate_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf327b19c85546a0","mcp_get_code":{"code_sha256":"bf327b19c85546a0"}},{"arxiv_id":"2406.05343","paper":"/paper/m3gia-a-cognition-inspired-multilingual-and","title":"M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songweii/M3GIA","path":"utils/data_utils.py","file_url":"https://github.com/songweii/M3GIA/blob/HEAD/utils/data_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":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2406.02548","paper":"/paper/open-yolo-3d-towards-fast-and-accurate-open","title":"Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aminebdj/openyolo3d","path":"run_evaluation.py","file_url":"https://github.com/aminebdj/openyolo3d/blob/HEAD/run_evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fd328d58844f055","mcp_get_code":{"code_sha256":"9fd328d58844f055"}},{"arxiv_id":"2405.18782","paper":"/paper/principled-probabilistic-imaging-using","title":"Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zihuiwu/PnP-DM-public","path":"pnpdm/models/edm/edm.py","file_url":"https://github.com/zihuiwu/PnP-DM-public/blob/HEAD/pnpdm/models/edm/edm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27ade2bc91298c8a","mcp_get_code":{"code_sha256":"27ade2bc91298c8a"}},{"arxiv_id":"2405.17111","paper":"/paper/diffusion-bridge-autoencoders-for","title":"Diffusion Bridge AutoEncoders for Unsupervised Representation Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ckczzj/PDAE","path":"trainer/train_representation_learning.py","file_url":"https://github.com/ckczzj/PDAE/blob/HEAD/trainer/train_representation_learning.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":"23a955cb5ab536ed","mcp_get_code":{"code_sha256":"23a955cb5ab536ed"}},{"arxiv_id":"2405.16749","paper":"/paper/dmplug-a-plug-in-method-for-solving-inverse","title":"DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sun-umn/DMPlug","path":"sr_inp_nonlinear.py","file_url":"https://github.com/sun-umn/DMPlug/blob/HEAD/sr_inp_nonlinear.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"77224ae152b1372f","mcp_get_code":{"code_sha256":"77224ae152b1372f"}},{"arxiv_id":"2405.10748","paper":"/paper/deep-data-consistency-a-fast-and-robust","title":"Deep Data Consistency: a Fast and Robust Diffusion Model-based Solver for Inverse Problems","date":"2024-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanyu-chen373/deepdataconsistency","path":"train_ddc.py","file_url":"https://github.com/hanyu-chen373/deepdataconsistency/blob/HEAD/train_ddc.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2405.01155","paper":"/paper/synflownet-towards-molecule-design-with","title":"SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mirunacrt/synflownet","path":"src/synflownet/config.py","file_url":"https://github.com/mirunacrt/synflownet/blob/HEAD/src/synflownet/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1aab6ebb1f26dd0","mcp_get_code":{"code_sha256":"a1aab6ebb1f26dd0"}},{"arxiv_id":"2404.08660","paper":"/paper/how-does-message-passing-improve","title":"How Does Message Passing Improve Collaborative Filtering?","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-research/test-time-aggregation-for-cf","path":"src/utils.py","file_url":"https://github.com/snap-research/test-time-aggregation-for-cf/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e57c202ae6aff88f","mcp_get_code":{"code_sha256":"e57c202ae6aff88f"}},{"arxiv_id":"2403.17042","paper":"/paper/provably-robust-score-based-diffusion","title":"Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction","date":"2024-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"x1xu/diffusion-plug-and-play","path":"sample_condition.py","file_url":"https://github.com/x1xu/diffusion-plug-and-play/blob/HEAD/sample_condition.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2403.11552","paper":"/paper/llm-3-large-language-model-based-task-and","title":"LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"assassinws/llm-tamp","path":"utils/io_util.py","file_url":"https://github.com/assassinws/llm-tamp/blob/HEAD/utils/io_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"090e4aa703a8043b","mcp_get_code":{"code_sha256":"090e4aa703a8043b"}},{"arxiv_id":"2402.16907","paper":"/paper/diffusion-posterior-proximal-sampling-for","title":"Diffusion Posterior Proximal Sampling for Image Restoration","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"74587887/dpps_code","path":"sample_condition.py","file_url":"https://github.com/74587887/dpps_code/blob/HEAD/sample_condition.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2402.11530","paper":"/paper/efficient-multimodal-learning-from-data","title":"Efficient Multimodal Learning from Data-centric Perspective","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baai-dcai/bunny","path":"bunny/eval/model_vqa_cmmmu.py","file_url":"https://github.com/baai-dcai/bunny/blob/HEAD/bunny/eval/model_vqa_cmmmu.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":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2402.06782","paper":"/paper/debating-with-more-persuasive-llms-leads-to","title":"Debating with More Persuasive LLMs Leads to More Truthful Answers","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucl-dark/llm_debate","path":"core/utils.py","file_url":"https://github.com/ucl-dark/llm_debate/blob/HEAD/core/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a055cc7323a5cf50","mcp_get_code":{"code_sha256":"a055cc7323a5cf50"}},{"arxiv_id":"2402.03099","paper":"/paper/intent-based-prompt-calibration-enhancing","title":"Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases","date":"2024-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladlev/autoprompt","path":"utils/config.py","file_url":"https://github.com/eladlev/autoprompt/blob/HEAD/utils/config.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":"f896b79b31f6b46c","mcp_get_code":{"code_sha256":"f896b79b31f6b46c"}},{"arxiv_id":"2401.08920","paper":"/paper/idempotence-and-perceptual-image-compression","title":"Idempotence and Perceptual Image Compression","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2312.16248","paper":"/paper/xuance-a-comprehensive-and-unified-deep","title":"XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agi-brain/xuance","path":"xuance/common/common_tools.py","file_url":"https://github.com/agi-brain/xuance/blob/HEAD/xuance/common/common_tools.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e426e46e07822b2","mcp_get_code":{"code_sha256":"7e426e46e07822b2"}},{"arxiv_id":"2310.04590","paper":"/paper/deep-model-predictive-optimization","title":"Deep Model Predictive Optimization","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jisacks/dmpo","path":"dmpo/utils.py","file_url":"https://github.com/jisacks/dmpo/blob/HEAD/dmpo/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"82eb3cd40fee08d1","mcp_get_code":{"code_sha256":"82eb3cd40fee08d1"}},{"arxiv_id":"2308.06053","paper":"/paper/cost-effective-on-device-continual-learning","title":"Cost-effective On-device Continual Learning over Memory Hierarchy with Miro","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omnia-unist/Miro","path":"config_manager.py","file_url":"https://github.com/omnia-unist/Miro/blob/HEAD/config_manager.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"474f2faee7e1748b","mcp_get_code":{"code_sha256":"474f2faee7e1748b"}},{"arxiv_id":"2307.13929","paper":"/paper/spatio-temporal-domain-awareness-for-multi","title":"Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starfdu1418/scope","path":"v2xvit/hypes_yaml/yaml_utils.py","file_url":"https://github.com/starfdu1418/scope/blob/HEAD/v2xvit/hypes_yaml/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":"c40c6561b37d440c","mcp_get_code":{"code_sha256":"c40c6561b37d440c"}},{"arxiv_id":"2211.15975","paper":"/paper/analyzing-infrastructure-lidar-placement-with","title":"Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pjlab-adg/lidarsimlib-and-placement-evaluation","path":"Placement-Evaluation/v2xvit/hypes_yaml/yaml_utils.py","file_url":"https://github.com/pjlab-adg/lidarsimlib-and-placement-evaluation/blob/HEAD/Placement-Evaluation/v2xvit/hypes_yaml/yaml_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":"c40c6561b37d440c","mcp_get_code":{"code_sha256":"c40c6561b37d440c"}},{"arxiv_id":"2210.08451","paper":"/paper/bridging-the-domain-gap-for-multi-agent","title":"Bridging the Domain Gap for Multi-Agent Perception","date":"2022-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"derrickxunu/mpda","path":"opencood/hypes_yaml/yaml_utils.py","file_url":"https://github.com/derrickxunu/mpda/blob/HEAD/opencood/hypes_yaml/yaml_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":"c40c6561b37d440c","mcp_get_code":{"code_sha256":"c40c6561b37d440c"}},{"arxiv_id":"2209.14687","paper":"/paper/diffusion-posterior-sampling-for-general","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","date":"2022-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DPS2022/diffusion-posterior-sampling","path":"sample_condition.py","file_url":"https://github.com/DPS2022/diffusion-posterior-sampling/blob/HEAD/sample_condition.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c67a2ab9d4b104","mcp_get_code":{"code_sha256":"00c67a2ab9d4b104"}},{"arxiv_id":"2206.08321","paper":"/paper/equivariant-descriptor-fields-se-3","title":"Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning","date":"2022-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tomato1mule/edf","path":"edf/data.py","file_url":"https://github.com/tomato1mule/edf/blob/HEAD/edf/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4883c352c5a1abf8","mcp_get_code":{"code_sha256":"4883c352c5a1abf8"}},{"arxiv_id":"2206.03314","paper":"/paper/integrating-random-effects-in-deep-neural","title":"Integrating Random Effects in Deep Neural Networks","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsimchoni/lmmnn","path":"simulate.py","file_url":"https://github.com/gsimchoni/lmmnn/blob/HEAD/simulate.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":"88c0d80fd3a000d0","mcp_get_code":{"code_sha256":"88c0d80fd3a000d0"}},{"arxiv_id":"2205.10920","paper":"/paper/test-time-robust-personalization-for","title":"Test-Time Robust Personalization for Federated Learning","date":"2022-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LINs-lab/FedTHE","path":"env/utils.py","file_url":"https://github.com/LINs-lab/FedTHE/blob/HEAD/env/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":"fcdbee2f89a2dda2","mcp_get_code":{"code_sha256":"fcdbee2f89a2dda2"}},{"arxiv_id":"2203.10638","paper":"/paper/v2x-vit-vehicle-to-everything-cooperative","title":"V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer","date":"2022-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DerrickXuNu/v2x-vit","path":"v2xvit/hypes_yaml/yaml_utils.py","file_url":"https://github.com/DerrickXuNu/v2x-vit/blob/HEAD/v2xvit/hypes_yaml/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":"c40c6561b37d440c","mcp_get_code":{"code_sha256":"c40c6561b37d440c"}},{"arxiv_id":"2201.02279","paper":"/paper/de-rendering-3d-objects-in-the-wild","title":"De-rendering 3D Objects in the Wild","date":"2022-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brummi/derender3d","path":"derender3d/utils.py","file_url":"https://github.com/brummi/derender3d/blob/HEAD/derender3d/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9d96366dfea8bc9","mcp_get_code":{"code_sha256":"c9d96366dfea8bc9"}},{"arxiv_id":"2112.09245","paper":"/paper/automated-deep-learning-neural-architecture","title":"Automated Deep Learning: Neural Architecture Search Is Not the End","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"D-X-Y/Awesome-AutoDL","path":"awesome_autodl/utils/yaml.py","file_url":"https://github.com/D-X-Y/Awesome-AutoDL/blob/HEAD/awesome_autodl/utils/yaml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb3be380e6653bdc","mcp_get_code":{"code_sha256":"bb3be380e6653bdc"}},{"arxiv_id":"2111.05897","paper":"/paper/persia-a-hybrid-system-scaling-deep-learning","title":"Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters","date":"2021-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"persiaml/persia","path":"persia/utils.py","file_url":"https://github.com/persiaml/persia/blob/HEAD/persia/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7a31b45dcb1335","mcp_get_code":{"code_sha256":"7e7a31b45dcb1335"}},{"arxiv_id":"2110.06169","paper":"/paper/offline-reinforcement-learning-with-implicit","title":"Offline Reinforcement Learning with Implicit Q-Learning","date":"2021-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dasgringuen/assetto_corsa_gym","path":"assetto_corsa_gym/AssettoCorsaEnv/ac_env.py","file_url":"https://github.com/dasgringuen/assetto_corsa_gym/blob/HEAD/assetto_corsa_gym/AssettoCorsaEnv/ac_env.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfb2071ad5f20847","mcp_get_code":{"code_sha256":"cfb2071ad5f20847"}},{"arxiv_id":"2109.02288","paper":"/paper/toward-realistic-single-view-3d-object","title":"Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images","date":"2021-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinairesearch/lemul","path":"utils.py","file_url":"https://github.com/vinairesearch/lemul/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":"c9d96366dfea8bc9","mcp_get_code":{"code_sha256":"c9d96366dfea8bc9"}},{"arxiv_id":"2107.02173","paper":"/paper/is-automated-topic-model-evaluation-broken","title":"Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence","date":"2021-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ahoho/topics","path":"runs/calculate_metrics.py","file_url":"https://github.com/ahoho/topics/blob/HEAD/runs/calculate_metrics.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":"08bfbf35112d40e0","mcp_get_code":{"code_sha256":"08bfbf35112d40e0"}},{"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":"8ac2b5acb8519f95","mcp_get_code":{"code_sha256":"8ac2b5acb8519f95"}},{"arxiv_id":"2104.14545","paper":"/paper/lighttrack-finding-lightweight-neural","title":"LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search","date":"2021-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/LightTrack","path":"lib/tracker/lighttrack.py","file_url":"https://github.com/researchmm/LightTrack/blob/HEAD/lib/tracker/lighttrack.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":"4b0995306a51fa38","mcp_get_code":{"code_sha256":"4b0995306a51fa38"}},{"arxiv_id":"2104.12476","paper":"/paper/eigengan-layer-wise-eigen-learning-for-gans","title":"EigenGAN: Layer-Wise Eigen-Learning for GANs","date":"2021-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LynnHo/EigenGAN-Tensorflow","path":"pylib/serialization.py","file_url":"https://github.com/LynnHo/EigenGAN-Tensorflow/blob/HEAD/pylib/serialization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f57b5b6af6b96676","mcp_get_code":{"code_sha256":"f57b5b6af6b96676"}},{"arxiv_id":"2104.04480","paper":"/paper/improving-the-efficiency-and-robustness-of-1","title":"Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features","date":"2021-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frederickszk/LRNet","path":"training/configs/loader.py","file_url":"https://github.com/frederickszk/LRNet/blob/HEAD/training/configs/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3bffc6edf7b94a8f","mcp_get_code":{"code_sha256":"3bffc6edf7b94a8f"}},{"arxiv_id":"2104.03954","paper":"/paper/de-rendering-the-world-s-revolutionary","title":"De-rendering the World's Revolutionary Artefacts","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elliottwu/sorderender","path":"derender/utils.py","file_url":"https://github.com/elliottwu/sorderender/blob/HEAD/derender/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9d96366dfea8bc9","mcp_get_code":{"code_sha256":"c9d96366dfea8bc9"}},{"arxiv_id":"2011.00844","paper":"/paper/do-2d-gans-know-3d-shape-unsupervised-3d-1","title":"Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs","date":"2020-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XingangPan/GAN2Shape","path":"gan2shape/utils.py","file_url":"https://github.com/XingangPan/GAN2Shape/blob/HEAD/gan2shape/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9d96366dfea8bc9","mcp_get_code":{"code_sha256":"c9d96366dfea8bc9"}},{"arxiv_id":"2008.01352","paper":"/paper/pde-driven-spatiotemporal-disentanglement","title":"PDE-Driven Spatiotemporal Disentanglement","date":"2020-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JeremDona/spatiotemporal_variable_separation","path":"var_sep/utils/helper.py","file_url":"https://github.com/JeremDona/spatiotemporal_variable_separation/blob/HEAD/var_sep/utils/helper.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":"2e294d738f6cc876","mcp_get_code":{"code_sha256":"2e294d738f6cc876"}},{"arxiv_id":"2006.05587","paper":"/paper/deep-neural-networks-for-the-sequential","title":"Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TaikiMiyagawa/SPRT-TANDEM","path":"utils/misc.py","file_url":"https://github.com/TaikiMiyagawa/SPRT-TANDEM/blob/HEAD/utils/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"393ae5de46d6fe89","mcp_get_code":{"code_sha256":"393ae5de46d6fe89"}},{"arxiv_id":"2005.09234","paper":"/paper/anomalous-sound-detection-based-on","title":"Anomalous sound detection based on interpolation deep neural network","date":"2020-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyoude/AE-ASD","path":"utils.py","file_url":"https://github.com/liuyoude/AE-ASD/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":"8ad197c793709725","mcp_get_code":{"code_sha256":"8ad197c793709725"}},{"arxiv_id":"2002.09219","paper":"/paper/stochastic-latent-residual-video-prediction-1","title":"Stochastic Latent Residual Video Prediction","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edouardelasalles/srvp","path":"helper.py","file_url":"https://github.com/edouardelasalles/srvp/blob/HEAD/helper.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":"2e294d738f6cc876","mcp_get_code":{"code_sha256":"2e294d738f6cc876"}},{"arxiv_id":"2001.06564","paper":"/paper/media-forensics-and-deepfakes-an-overview","title":"Media Forensics and DeepFakes: an overview","date":"2020-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yizhe-ang/fake-detection-lab","path":"src/utils.py","file_url":"https://github.com/yizhe-ang/fake-detection-lab/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":"7777d80494dc0100","mcp_get_code":{"code_sha256":"7777d80494dc0100"}},{"arxiv_id":"1911.11130","paper":"/paper/unsupervised-learning-of-probably-symmetric","title":"Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild","date":"2019-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elliottwu/unsup3d","path":"unsup3d/utils.py","file_url":"https://github.com/elliottwu/unsup3d/blob/HEAD/unsup3d/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9d96366dfea8bc9","mcp_get_code":{"code_sha256":"c9d96366dfea8bc9"}},{"arxiv_id":"1907.05737","paper":"/paper/pc-darts-partial-channel-connections-for","title":"PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search","date":"2019-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peteryuX/pcdarts-tf2","path":"modules/utils.py","file_url":"https://github.com/peteryuX/pcdarts-tf2/blob/HEAD/modules/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6d8e748cf074438","mcp_get_code":{"code_sha256":"c6d8e748cf074438"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"narumiruna/efficientnet-pytorch","path":"src/efficientnet/utils/utils.py","file_url":"https://github.com/narumiruna/efficientnet-pytorch/blob/HEAD/src/efficientnet/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":"8fdd37211c0c6646","mcp_get_code":{"code_sha256":"8fdd37211c0c6646"}},{"arxiv_id":"1901.08753","paper":"/paper/towards-a-deeper-understanding-of-adversarial","title":"On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salu133445/dan","path":"src/dan/utils.py","file_url":"https://github.com/salu133445/dan/blob/HEAD/src/dan/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9b277604f711d0d","mcp_get_code":{"code_sha256":"e9b277604f711d0d"}},{"arxiv_id":"1901.02401","paper":"/paper/the-buzzard-flock-dark-energy-survey","title":"The Buzzard Flock: Dark Energy Survey Synthetic Sky Catalogs","date":null,"month_inferred_from_arxiv_id":"2019-01","title_source":"archive","repo":"LSSTDESC/gcr-catalogs","path":"GCRCatalogs/catalog_helpers.py","file_url":"https://github.com/LSSTDESC/gcr-catalogs/blob/HEAD/GCRCatalogs/catalog_helpers.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":"e894ad923f1f62f6","mcp_get_code":{"code_sha256":"e894ad923f1f62f6"}},{"arxiv_id":"1812.01387","paper":"/paper/estimating-6d-pose-from-localizing-designated","title":"Estimating 6D Pose From Localizing Designated Surface Keypoints","date":"2018-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hz-ants/betapose","path":"3_6Dpose_estimator/betapose_evaluate.py","file_url":"https://github.com/hz-ants/betapose/blob/HEAD/3_6Dpose_estimator/betapose_evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42001485c89d0e7f","mcp_get_code":{"code_sha256":"42001485c89d0e7f"}},{"arxiv_id":"1810.03065","paper":"/paper/deep-model-based-6d-pose-refinement-in-rgb","title":"Deep Model-Based 6D Pose Refinement in RGB","date":"2018-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fabi92/eccv18-rgb_pose_refinement","path":"utils/sixd.py","file_url":"https://github.com/fabi92/eccv18-rgb_pose_refinement/blob/HEAD/utils/sixd.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42001485c89d0e7f","mcp_get_code":{"code_sha256":"42001485c89d0e7f"}},{"arxiv_id":"1809.00219","paper":"/paper/esrgan-enhanced-super-resolution-generative","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","date":"2018-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peteryuX/esrgan-tf2","path":"modules/utils.py","file_url":"https://github.com/peteryuX/esrgan-tf2/blob/HEAD/modules/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6d8e748cf074438","mcp_get_code":{"code_sha256":"c6d8e748cf074438"}},{"arxiv_id":"1806.07366","paper":"/paper/neural-ordinary-differential-equations","title":"Neural Ordinary Differential Equations","date":"2018-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boschresearch/numerics_independent_neural_odes","path":"models/models.py","file_url":"https://github.com/boschresearch/numerics_independent_neural_odes/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"0e64c9442199a010","mcp_get_code":{"code_sha256":"0e64c9442199a010"}},{"arxiv_id":"1804.09399","paper":"/paper/convolutional-generative-adversarial-networks","title":"Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation","date":"2018-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salu133445/musegan","path":"src/musegan/utils.py","file_url":"https://github.com/salu133445/musegan/blob/HEAD/src/musegan/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9b277604f711d0d","mcp_get_code":{"code_sha256":"e9b277604f711d0d"}},{"arxiv_id":"1803.04996","paper":"/paper/comparing-task-simplifications-to-learn","title":"Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2018-03","title_source":"archive","repo":"BarisYazici/deep-rl-grasping","path":"manipulation_main/common/io_utils.py","file_url":"https://github.com/BarisYazici/deep-rl-grasping/blob/HEAD/manipulation_main/common/io_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90cd690560088629","mcp_get_code":{"code_sha256":"90cd690560088629"}},{"arxiv_id":"1703.08603","paper":"/paper/adversarial-examples-for-semantic","title":"Adversarial Examples for Semantic Segmentation and Object Detection","date":"2017-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yizhe-ang/detectron2-1","path":"train_net.py","file_url":"https://github.com/yizhe-ang/detectron2-1/blob/HEAD/train_net.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":"7ac7d78c168ae77b","mcp_get_code":{"code_sha256":"7ac7d78c168ae77b"}},{"arxiv_id":"1603.08486","paper":"/paper/learning-to-read-chest-x-rays-recurrent","title":"Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation","date":"2016-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pvtien96/CXRAbnormalityLocalization","path":"src/FasterRCNN/utils.py","file_url":"https://github.com/pvtien96/CXRAbnormalityLocalization/blob/HEAD/src/FasterRCNN/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":"16e57445a86f69aa","mcp_get_code":{"code_sha256":"16e57445a86f69aa"}},{"arxiv_id":"1411.1784","paper":"/paper/conditional-generative-adversarial-nets","title":"Conditional Generative Adversarial Nets","date":"2014-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lornatang/CGAN-PyTorch","path":"conditional_gan/utils/ops.py","file_url":"https://github.com/Lornatang/CGAN-PyTorch/blob/HEAD/conditional_gan/utils/ops.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":"865e39ac4e96e241","mcp_get_code":{"code_sha256":"865e39ac4e96e241"}},{"arxiv_id":"Yang_PVC_Progressive_Visual_Token_Compression_for_Unified_Image_and_Video_CVPR_2025_paper","paper":null,"title":"arXiv:Yang_PVC_Progressive_Visual_Token_Compression_for_Unified_Image_and_Video_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"OpenGVLab/PVC","path":"eval/mmmu/data_utils.py","file_url":"https://github.com/OpenGVLab/PVC/blob/HEAD/eval/mmmu/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce93b7e192e1a8b7","mcp_get_code":{"code_sha256":"ce93b7e192e1a8b7"}},{"arxiv_id":"2025.findings-emnlp.103","paper":null,"title":"arXiv:2025.findings-emnlp.103","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yha9806/VULCA-EMNLP2025","path":"src/utils.py","file_url":"https://github.com/yha9806/VULCA-EMNLP2025/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":"0b02aaf829f65a9e","mcp_get_code":{"code_sha256":"0b02aaf829f65a9e"}}]}