{"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/get-logger","entry":"get_logger","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":383,"n_papers_ran":160,"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":287,"n_samples_ran":102,"n_samples_fingerprinted":1,"n_places":391,"n_places_pointer_only":100,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":28,"ran_fixture":0,"ran":73,"unverified":185},"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":"2608.14953","paper":"/paper/arxiv-2608-14953","title":"T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"BiSheng-Compiler-Agents/CCE-WOZ","path":"kernel_mas/src/utils/logger.py","file_url":"https://github.com/BiSheng-Compiler-Agents/CCE-WOZ/blob/HEAD/kernel_mas/src/utils/logger.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":"3ed45fd7e156ee72","mcp_get_code":{"code_sha256":"3ed45fd7e156ee72"}},{"arxiv_id":"2608.07763","paper":"/paper/arxiv-2608-07763","title":"Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"NASK-NLP/PoVisLE","path":"povisle/logger.py","file_url":"https://github.com/NASK-NLP/PoVisLE/blob/HEAD/povisle/logger.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":"b6e4407d4a83393a","mcp_get_code":{"code_sha256":"b6e4407d4a83393a"}},{"arxiv_id":"2607.22552","paper":"/paper/arxiv-2607-22552","title":"MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"NicolasSR/MioFFAn","path":"lib/logger.py","file_url":"https://github.com/NicolasSR/MioFFAn/blob/HEAD/lib/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8f8488cc8f9d285","mcp_get_code":{"code_sha256":"e8f8488cc8f9d285"}},{"arxiv_id":"2607.11423","paper":"/paper/arxiv-2607-11423","title":"TOFU: A White-Box, Token-Efficient Agent Harness for Researchers","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"NiuTrans/ToFu","path":"tofu_agent/runtime.py","file_url":"https://github.com/NiuTrans/ToFu/blob/HEAD/tofu_agent/runtime.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":"8a145e8f9fededf5","mcp_get_code":{"code_sha256":"8a145e8f9fededf5"}},{"arxiv_id":"2607.03248","paper":"/paper/arxiv-2607-03248","title":"Unbiased Alignment for Large Language Models with Noisy Preferences","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"cswjl/unbiased-alignment","path":"utils.py","file_url":"https://github.com/cswjl/unbiased-alignment/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":"ef4914a3d58f1246","mcp_get_code":{"code_sha256":"ef4914a3d58f1246"}},{"arxiv_id":"2606.21092","paper":"/paper/arxiv-2606-21092","title":"BASIL: BAYESIAN APPLICATION FOR SCIENTIFIC ITERATION AND LEARNING PREPRINT","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"molecularmodelinglab/BASIL","path":"app/logging_config.py","file_url":"https://github.com/molecularmodelinglab/BASIL/blob/HEAD/app/logging_config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"694ddd1eca095557","mcp_get_code":{"code_sha256":"694ddd1eca095557"}},{"arxiv_id":"2606.03391","paper":"/paper/arxiv-2606-03391","title":"When Model Merging Breaks Routing: Training-Free Calibration for MoE","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"huangcb01/HARC","path":"src/utils.py","file_url":"https://github.com/huangcb01/HARC/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":"7eef7deaf26f4e51","mcp_get_code":{"code_sha256":"7eef7deaf26f4e51"}},{"arxiv_id":"2605.16941","paper":"/paper/arxiv-2605-16941","title":"Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Feng-Hong/WINO-DLLM","path":"MMaDA/models/logging.py","file_url":"https://github.com/Feng-Hong/WINO-DLLM/blob/HEAD/MMaDA/models/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2605.15284","paper":"/paper/arxiv-2605-15284","title":"Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"tum-pbs/tadpole","path":"tadpole/utils.py","file_url":"https://github.com/tum-pbs/tadpole/blob/HEAD/tadpole/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":"c96bfb71837d54ff","mcp_get_code":{"code_sha256":"c96bfb71837d54ff"}},{"arxiv_id":"2605.07315","paper":"/paper/arxiv-2605-07315","title":"LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"TioeAre/LaTER","path":"later/src/train/log.py","file_url":"https://github.com/TioeAre/LaTER/blob/HEAD/later/src/train/log.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":"1082508e65f77331","mcp_get_code":{"code_sha256":"1082508e65f77331"}},{"arxiv_id":"2605.06788","paper":"/paper/arxiv-2605-06788","title":"Conformal Agent Error Attribution","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"layer6ai-labs/conformal-agent-error-attribution","path":"src/conformal/filtering_conformal.py","file_url":"https://github.com/layer6ai-labs/conformal-agent-error-attribution/blob/HEAD/src/conformal/filtering_conformal.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":"cfd4ab2359fb2705","mcp_get_code":{"code_sha256":"cfd4ab2359fb2705"}},{"arxiv_id":"2605.01350","paper":"/paper/arxiv-2605-01350","title":"LLM Output Detectability and Task Performance Can be Jointly Optimized","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"pakapaka333/PUPPET","path":"experimental/common/utils.py","file_url":"https://github.com/pakapaka333/PUPPET/blob/HEAD/experimental/common/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":"ce0aca0922378f4d","mcp_get_code":{"code_sha256":"ce0aca0922378f4d"}},{"arxiv_id":"2604.24720","paper":"/paper/arxiv-2604-24720","title":"Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ikii-sd/pba2026-crazyrichteam","path":"src/logger.py","file_url":"https://github.com/ikii-sd/pba2026-crazyrichteam/blob/HEAD/src/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d016d475578a1d97","mcp_get_code":{"code_sha256":"d016d475578a1d97"}},{"arxiv_id":"2604.23699","paper":"/paper/arxiv-2604-23699","title":"Beyond coauthorship: semantic structure and phantom collaborators in transportation research, 1967-2025","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"UMN-Choi-Lab/transport-atlas","path":"src/transport_atlas/utils/logger.py","file_url":"https://github.com/UMN-Choi-Lab/transport-atlas/blob/HEAD/src/transport_atlas/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ee85429f52ae993","mcp_get_code":{"code_sha256":"1ee85429f52ae993"}},{"arxiv_id":"2604.11628","paper":"/paper/arxiv-2604-11628","title":"Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"qingyue2014/Rsum","path":"config.py","file_url":"https://github.com/qingyue2014/Rsum/blob/HEAD/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1fa4185c309e6ea7","mcp_get_code":{"code_sha256":"1fa4185c309e6ea7"}},{"arxiv_id":"2604.09041","paper":"/paper/arxiv-2604-09041","title":"U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Rose-STL-Lab/u-cast","path":"src/models/networks_edm.py","file_url":"https://github.com/Rose-STL-Lab/u-cast/blob/HEAD/src/models/networks_edm.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":"fd84c604a3d07817","mcp_get_code":{"code_sha256":"fd84c604a3d07817"}},{"arxiv_id":"2604.02051","paper":"/paper/arxiv-2604-02051","title":"OUROBOROS: Dynamic Weight Generation for Recursive Transformers via Input-Conditioned LoRA Modulation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"RightNow-AI/ouroboros","path":"ouroboros/utils.py","file_url":"https://github.com/RightNow-AI/ouroboros/blob/HEAD/ouroboros/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"bb8e1a3fd289bcce","mcp_get_code":{"code_sha256":"bb8e1a3fd289bcce"}},{"arxiv_id":"2603.27303","paper":"/paper/arxiv-2603-27303","title":"Self-evolving AI agents for protein discovery and directed evolution","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"ai4protein/VenusFactory2","path":"src/logger.py","file_url":"https://github.com/ai4protein/VenusFactory2/blob/HEAD/src/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6a6886c1dac14674","mcp_get_code":{"code_sha256":"6a6886c1dac14674"}},{"arxiv_id":"2603.22323","paper":"/paper/arxiv-2603-22323","title":"A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"wang-fujin/PINN4SOH","path":"Model/Model.py","file_url":"https://github.com/wang-fujin/PINN4SOH/blob/HEAD/Model/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":"c85d1fcdc4292660","mcp_get_code":{"code_sha256":"c85d1fcdc4292660"}},{"arxiv_id":"2603.21970","paper":"/paper/arxiv-2603-21970","title":"Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"eracoding/llm-medical-summarization","path":"src/utils/logging_utils.py","file_url":"https://github.com/eracoding/llm-medical-summarization/blob/HEAD/src/utils/logging_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1082508e65f77331","mcp_get_code":{"code_sha256":"1082508e65f77331"}},{"arxiv_id":"2603.18872","paper":"/paper/arxiv-2603-18872","title":"DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"blessonvar/DriftGuard","path":"src/driftguard/config.py","file_url":"https://github.com/blessonvar/DriftGuard/blob/HEAD/src/driftguard/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"553cb8aa327118fa","mcp_get_code":{"code_sha256":"553cb8aa327118fa"}},{"arxiv_id":"2603.09821","paper":"/paper/arxiv-2603-09821","title":"One-Eval: An Agentic System for Automated and Traceable LLM Evaluation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"OpenDCAI/One-Eval","path":"one_eval/logger.py","file_url":"https://github.com/OpenDCAI/One-Eval/blob/HEAD/one_eval/logger.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":"fb0c7030b5b1e6b3","mcp_get_code":{"code_sha256":"fb0c7030b5b1e6b3"}},{"arxiv_id":"2603.03328","paper":"/paper/arxiv-2603-03328","title":"StructLens: A Structural Lens for Language Models via Maximum Spanning Trees","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"naist-nlp/structlens","path":"src/structlens/utils/logging_config.py","file_url":"https://github.com/naist-nlp/structlens/blob/HEAD/src/structlens/utils/logging_config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d3ecc9fce1c2ffe","mcp_get_code":{"code_sha256":"6d3ecc9fce1c2ffe"}},{"arxiv_id":"2602.18823","paper":"/paper/arxiv-2602-18823","title":"EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"nhsengland/evalsense","path":"evalsense/logging.py","file_url":"https://github.com/nhsengland/evalsense/blob/HEAD/evalsense/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7f2ffee57bea504","mcp_get_code":{"code_sha256":"d7f2ffee57bea504"}},{"arxiv_id":"2602.15898","paper":"/paper/arxiv-2602-15898","title":"MultiCube-RAG for Multi-hop Question Answering","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"OpenBMB/UltraRAG","path":"src/ultrarag/mcp_logging.py","file_url":"https://github.com/OpenBMB/UltraRAG/blob/HEAD/src/ultrarag/mcp_logging.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":"9e03092ffc8351c2","mcp_get_code":{"code_sha256":"9e03092ffc8351c2"}},{"arxiv_id":"2602.14849","paper":"/paper/arxiv-2602-14849","title":"Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"mpi-dsg/atomix","path":"src/atomix/logging.py","file_url":"https://github.com/mpi-dsg/atomix/blob/HEAD/src/atomix/logging.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":"5c22288de7d5c59c","mcp_get_code":{"code_sha256":"5c22288de7d5c59c"}},{"arxiv_id":"2602.12241","paper":"/paper/arxiv-2602-12241","title":"Moonshine v2: Ergodic Streaming Encoder ASR for Latency-Critical Speech Applications","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"SYSTRAN/faster-whisper","path":"benchmark/utils.py","file_url":"https://github.com/SYSTRAN/faster-whisper/blob/HEAD/benchmark/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce80eafaf33fae23","mcp_get_code":{"code_sha256":"ce80eafaf33fae23"}},{"arxiv_id":"2602.11354","paper":"/paper/arxiv-2602-11354","title":"ReplicatorBench: Benchmarking LLM Agents for Replicability in Social and Behavioral Sciences","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"CenterForOpenScience/llm-benchmarking","path":"replicatorbench/core/utils.py","file_url":"https://github.com/CenterForOpenScience/llm-benchmarking/blob/HEAD/replicatorbench/core/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":"f4404bef1f57ae8f","mcp_get_code":{"code_sha256":"f4404bef1f57ae8f"}},{"arxiv_id":"2602.07800","paper":"/paper/arxiv-2602-07800","title":"Approximating Matrix Functions with Deep Neural Networks and Transformers","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"rahul3/LAWT","path":"src/neuralnet/common.py","file_url":"https://github.com/rahul3/LAWT/blob/HEAD/src/neuralnet/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"122fce61b2b8891b","mcp_get_code":{"code_sha256":"122fce61b2b8891b"}},{"arxiv_id":"2602.02600","paper":"/paper/arxiv-2602-02600","title":"Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models","date":"2026-02-01","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"shuita2333/PAD-codes","path":"MMaDA-PAD/models/logging.py","file_url":"https://github.com/shuita2333/PAD-codes/blob/HEAD/MMaDA-PAD/models/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2601.19040","paper":"/paper/arxiv-2601-19040","title":"OATS: Online Data Augmentation for Time Series Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"microsoft/TimeCraft","path":"Diff-MN/lib/utils.py","file_url":"https://github.com/microsoft/TimeCraft/blob/HEAD/Diff-MN/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":"6e6961df228cbc5a","mcp_get_code":{"code_sha256":"6e6961df228cbc5a"}},{"arxiv_id":"2601.10804","paper":"/paper/arxiv-2601-10804","title":"BYOL: Bring Your Own Language Into LLMs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"microsoft/byol","path":"byol/common/logging.py","file_url":"https://github.com/microsoft/byol/blob/HEAD/byol/common/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d6726ade42018b2","mcp_get_code":{"code_sha256":"8d6726ade42018b2"}},{"arxiv_id":"2601.10477","paper":"/paper/arxiv-2601-10477","title":"Urban Socio-Semantic Segmentation with Vision-Language Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"AMAP-ML/SocioReasoner","path":"mcore_adapter/src/mcore_adapter/utils.py","file_url":"https://github.com/AMAP-ML/SocioReasoner/blob/HEAD/mcore_adapter/src/mcore_adapter/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":"0ca242314116dc9c","mcp_get_code":{"code_sha256":"0ca242314116dc9c"}},{"arxiv_id":"2601.05212","paper":"/paper/arxiv-2601-05212","title":"FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"sisinflab/FlowLet","path":"flowlet/models/flow_matching.py","file_url":"https://github.com/sisinflab/FlowLet/blob/HEAD/flowlet/models/flow_matching.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":"755cbe5d70752c6a","mcp_get_code":{"code_sha256":"755cbe5d70752c6a"}},{"arxiv_id":"2510.20414","paper":"/paper/arxiv-2510-20414","title":"Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"undes1red/IFNMTPP","path":"src/taskhost_utils.py","file_url":"https://github.com/undes1red/IFNMTPP/blob/HEAD/src/taskhost_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7552f783dcf6a118","mcp_get_code":{"code_sha256":"7552f783dcf6a118"}},{"arxiv_id":"2510.13887","paper":"/paper/arxiv-2510-13887","title":"Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"XiaojianDing/2025-NeurIPS-HSACC","path":"utils/logger_.py","file_url":"https://github.com/XiaojianDing/2025-NeurIPS-HSACC/blob/HEAD/utils/logger_.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d7f91cdb9f651567","mcp_get_code":{"code_sha256":"d7f91cdb9f651567"}},{"arxiv_id":"2510.00523","paper":"/paper/arxiv-2510-00523","title":"VIRTUE: Visual-Interactive Text-Image Universal Embedder","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"sony/virtue","path":"codes/src/logging.py","file_url":"https://github.com/sony/virtue/blob/HEAD/codes/src/logging.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":"edac44cad40a9c53","mcp_get_code":{"code_sha256":"edac44cad40a9c53"}},{"arxiv_id":"2509.26224","paper":"/paper/arxiv-2509-26224","title":"Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"sisinflab/tyler","path":"ITLP/logger.py","file_url":"https://github.com/sisinflab/tyler/blob/HEAD/ITLP/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"564280e1b0723557","mcp_get_code":{"code_sha256":"564280e1b0723557"}},{"arxiv_id":"2509.18577","paper":"/paper/arxiv-2509-18577","title":"Prior-based Noisy Text Data Filtering: Fast and Strong Alternative For Perplexity","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"ybseo-ac/prior_filter","path":"utils.py","file_url":"https://github.com/ybseo-ac/prior_filter/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":"60dbd956810fafb6","mcp_get_code":{"code_sha256":"60dbd956810fafb6"}},{"arxiv_id":"2509.15857","paper":"/paper/arxiv-2509-15857","title":"EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"Kotoge/EvoBrain","path":"model/s4.py","file_url":"https://github.com/Kotoge/EvoBrain/blob/HEAD/model/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2507.14681","paper":null,"title":"arXiv:2507.14681","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"almeidava93/llm-as-code-selectors-paper","path":"log.py","file_url":"https://github.com/almeidava93/llm-as-code-selectors-paper/blob/HEAD/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24ade9eb8c297fe7","mcp_get_code":{"code_sha256":"24ade9eb8c297fe7"}},{"arxiv_id":"2507.12465","paper":"/paper/physx-physical-grounded-3d-asset-generation","title":"PhysX: Physical-Grounded 3D Asset Generation","date":"2025-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ziangcao0312/PhysX","path":"example.py","file_url":"https://github.com/ziangcao0312/PhysX/blob/HEAD/example.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2507.11097","paper":"/paper/the-devil-behind-the-mask-an-emergent-safety","title":"The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs","date":"2025-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zichenwen1/dija","path":"MMaDA/models/logging.py","file_url":"https://github.com/zichenwen1/dija/blob/HEAD/MMaDA/models/logging.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":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2507.01006","paper":"/paper/glm-4-1v-thinking-towards-versatile","title":"GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","date":"2025-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/glm-4.1v-thinking","path":"glmv_reward/src/glmv_reward/utils/logging.py","file_url":"https://github.com/thudm/glm-4.1v-thinking/blob/HEAD/glmv_reward/src/glmv_reward/utils/logging.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":"e44711e0f5fff99f","mcp_get_code":{"code_sha256":"e44711e0f5fff99f"}},{"arxiv_id":"2506.22375","paper":null,"title":"arXiv:2506.22375","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"handsome999KK/GSP_OOD","path":"models/pointbert/logger.py","file_url":"https://github.com/handsome999KK/GSP_OOD/blob/HEAD/models/pointbert/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13248d61d84b9cee","mcp_get_code":{"code_sha256":"13248d61d84b9cee"}},{"arxiv_id":"2505.23564","paper":"/paper/segment-policy-optimization-effective-segment","title":"Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIFrameResearch/SPO","path":"src/treetune/logging_utils.py","file_url":"https://github.com/AIFrameResearch/SPO/blob/HEAD/src/treetune/logging_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e635b6a7bc9b6a4f","mcp_get_code":{"code_sha256":"e635b6a7bc9b6a4f"}},{"arxiv_id":"2505.23380","paper":"/paper/unirl-self-improving-unified-multimodal","title":"UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/unirl","path":"models/logging.py","file_url":"https://github.com/showlab/unirl/blob/HEAD/models/logging.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":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2505.18909","paper":"/paper/on-the-role-of-label-noise-in-the-feature","title":"On the Role of Label Noise in the Feature Learning Process","date":"2025-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzp1012/label-noise-theory","path":"src/utils/utils.py","file_url":"https://github.com/zzp1012/label-noise-theory/blob/HEAD/src/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edd9c782220bb4e8","mcp_get_code":{"code_sha256":"edd9c782220bb4e8"}},{"arxiv_id":"2505.18883","paper":"/paper/partition-generative-modeling-masked-modeling","title":"Partition Generative Modeling: Masked Modeling Without Masks","date":"2025-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kuleshov-group/mdlm","path":"utils.py","file_url":"https://github.com/kuleshov-group/mdlm/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":"60dbd956810fafb6","mcp_get_code":{"code_sha256":"60dbd956810fafb6"}},{"arxiv_id":"2505.15809","paper":"/paper/mmada-multimodal-large-diffusion-language","title":"MMaDA: Multimodal Large Diffusion Language Models","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gen-Verse/MMaDA","path":"models/logging.py","file_url":"https://github.com/Gen-Verse/MMaDA/blob/HEAD/models/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2505.14828","paper":"/paper/deep-koopman-operator-framework-for-causal","title":"Deep Koopman operator framework for causal discovery in nonlinear dynamical systems","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juannat7/kausal","path":"kausal/utils.py","file_url":"https://github.com/juannat7/kausal/blob/HEAD/kausal/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7241207db6d0903","mcp_get_code":{"code_sha256":"f7241207db6d0903"}},{"arxiv_id":"2505.14671","paper":"/paper/unictokens-boosting-personalized","title":"UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arctanxarc/unictokens","path":"models/logging.py","file_url":"https://github.com/arctanxarc/unictokens/blob/HEAD/models/logging.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":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2505.04331","paper":"/paper/neural-representational-consistency-emerges","title":"Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuyu-cs/pnba","path":"extract_neural_rep.py","file_url":"https://github.com/zhuyu-cs/pnba/blob/HEAD/extract_neural_rep.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":"d0267a090194b190","mcp_get_code":{"code_sha256":"d0267a090194b190"}},{"arxiv_id":"2504.07957","paper":"/paper/mm-ifengine-towards-multimodal-instruction","title":"MM-IFEngine: Towards Multimodal Instruction Following","date":"2025-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SYuan03/MM-IFEngine","path":"data_gen/utils/log.py","file_url":"https://github.com/SYuan03/MM-IFEngine/blob/HEAD/data_gen/utils/log.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":"7886dadebf6b2bb6","mcp_get_code":{"code_sha256":"7886dadebf6b2bb6"}},{"arxiv_id":"2503.15621","paper":"/paper/llava-more-a-comparative-study-of-llms-and","title":"LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning","date":"2025-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/LLaVA-MORE","path":"src/llava/utils.py","file_url":"https://github.com/aimagelab/LLaVA-MORE/blob/HEAD/src/llava/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":"d76907fa6a0240e6","mcp_get_code":{"code_sha256":"d76907fa6a0240e6"}},{"arxiv_id":"2503.15477","paper":"/paper/what-makes-a-reward-model-a-good-teacher-an","title":"What Makes a Reward Model a Good Teacher? An Optimization Perspective","date":"2025-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-pli/what-makes-good-rm","path":"src/what_makes_good_rm/Utils/logger.py","file_url":"https://github.com/princeton-pli/what-makes-good-rm/blob/HEAD/src/what_makes_good_rm/Utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2188119dc834b8a0","mcp_get_code":{"code_sha256":"2188119dc834b8a0"}},{"arxiv_id":"2503.13828","paper":"/paper/scale-aware-contrastive-reverse-distillation","title":"Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection","date":"2025-03-18","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":"a1db6c55cfde6e19","mcp_get_code":{"code_sha256":"a1db6c55cfde6e19"}},{"arxiv_id":"2503.08363","paper":"/paper/parametric-point-cloud-completion-for","title":"Parametric Point Cloud Completion for Polygonal Surface Reconstruction","date":"2025-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"parametric-completion/paco","path":"utils/logger.py","file_url":"https://github.com/parametric-completion/paco/blob/HEAD/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a5f762df7373136a","mcp_get_code":{"code_sha256":"a5f762df7373136a"}},{"arxiv_id":"2503.06252","paper":"/paper/can-atomic-step-decomposition-enhance-the","title":"Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?","date":"2025-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quinn777/atomthink","path":"src/llamafactory/extras/logging.py","file_url":"https://github.com/quinn777/atomthink/blob/HEAD/src/llamafactory/extras/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5245a66e965ad53b","mcp_get_code":{"code_sha256":"5245a66e965ad53b"}},{"arxiv_id":"2503.03651","paper":"/paper/doracycle-domain-oriented-adaptation-of","title":"DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles","date":"2025-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/DoraCycle","path":"models/logging.py","file_url":"https://github.com/showlab/DoraCycle/blob/HEAD/models/logging.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":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2502.19805","paper":"/paper/implicit-search-via-discrete-diffusion-a","title":"Implicit Search via Discrete Diffusion: A Study on Chess","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUNLP/DiffuSearch","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/HKUNLP/DiffuSearch/blob/HEAD/src/llmtuner/extras/logging.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":"56faa444d1b260c6","mcp_get_code":{"code_sha256":"56faa444d1b260c6"}},{"arxiv_id":"2502.14911","paper":null,"title":"arXiv:2502.14911","date":null,"month_inferred_from_arxiv_id":"2025-02","title_source":null,"repo":"aisingapore/sea-helm","path":"src/base_logger.py","file_url":"https://github.com/aisingapore/sea-helm/blob/HEAD/src/base_logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f806529d1dd7024","mcp_get_code":{"code_sha256":"7f806529d1dd7024"}},{"arxiv_id":"2502.01384","paper":"/paper/fine-tuning-discrete-diffusion-models-with","title":"Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ozekri/SEPO","path":"GRPO_MDLM_DNA/utils.py","file_url":"https://github.com/ozekri/SEPO/blob/HEAD/GRPO_MDLM_DNA/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"60dbd956810fafb6","mcp_get_code":{"code_sha256":"60dbd956810fafb6"}},{"arxiv_id":"2501.09620","paper":"/paper/beyond-reward-hacking-causal-rewards-for","title":"Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment","date":"2025-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tatsu-lab/alpaca_farm","path":"src/alpaca_farm/logging.py","file_url":"https://github.com/tatsu-lab/alpaca_farm/blob/HEAD/src/alpaca_farm/logging.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":"043cf4a6fd60480b","mcp_get_code":{"code_sha256":"043cf4a6fd60480b"}},{"arxiv_id":"2501.05901","paper":"/paper/valley2-exploring-multimodal-models-with","title":"Valley2: Exploring Multimodal Models with Scalable Vision-Language Design","date":"2025-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/valley","path":"valley/utils.py","file_url":"https://github.com/bytedance/valley/blob/HEAD/valley/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":"bc264e58e84dcfbe","mcp_get_code":{"code_sha256":"bc264e58e84dcfbe"}},{"arxiv_id":"2412.13670","paper":"/paper/antileak-bench-preventing-data-contamination","title":"AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge","date":"2024-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bobxwu/antileak-bench","path":"utils/logger.py","file_url":"https://github.com/bobxwu/antileak-bench/blob/HEAD/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fde7564d5daa655","mcp_get_code":{"code_sha256":"8fde7564d5daa655"}},{"arxiv_id":"2412.13420","paper":"/paper/botsim-llm-powered-malicious-social-botnet","title":"BotSim: LLM-Powered Malicious Social Botnet Simulation","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"qqqqqqby/botsim","path":"BotSim-24-Exp/Method/GNN-Based/BotRGCN.py","file_url":"https://github.com/qqqqqqby/botsim/blob/HEAD/BotSim-24-Exp/Method/GNN-Based/BotRGCN.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":"59920b9f51b00bf6","mcp_get_code":{"code_sha256":"59920b9f51b00bf6"}},{"arxiv_id":"2412.09805","paper":"/paper/universal-inceptive-gnns-by-eliminating-the","title":"Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"galogm/IGNN","path":"ignn/utils/logging.py","file_url":"https://github.com/galogm/IGNN/blob/HEAD/ignn/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e68afef9ccd7352","mcp_get_code":{"code_sha256":"7e68afef9ccd7352"}},{"arxiv_id":"2412.04887","paper":"/paper/momentum-gs-momentum-gaussian-self","title":"Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jixuan-Fan/Momentum-GS","path":"train_coarse.py","file_url":"https://github.com/Jixuan-Fan/Momentum-GS/blob/HEAD/train_coarse.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c43eefbf29da02a2","mcp_get_code":{"code_sha256":"c43eefbf29da02a2"}},{"arxiv_id":"2412.03187","paper":"/paper/weighted-reward-preference-optimization-for","title":"Weighted-Reward Preference Optimization for Implicit Model Fusion","date":"2024-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanqiwan/fusellm","path":"FuseLLM/src/utils/others.py","file_url":"https://github.com/fanqiwan/fusellm/blob/HEAD/FuseLLM/src/utils/others.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1082508e65f77331","mcp_get_code":{"code_sha256":"1082508e65f77331"}},{"arxiv_id":"2412.02723","paper":"/paper/dyffcast-regional-precipitation-nowcasting","title":"DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dseal95/dyffcast","path":"rainnow/src/loss.py","file_url":"https://github.com/dseal95/dyffcast/blob/HEAD/rainnow/src/loss.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":"8c82bf49e6813501","mcp_get_code":{"code_sha256":"8c82bf49e6813501"}},{"arxiv_id":"2412.01129","paper":"/paper/rilq-rank-insensitive-lora-based-quantization","title":"RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aiha-lab/rilq","path":"rilq_utils/utils.py","file_url":"https://github.com/aiha-lab/rilq/blob/HEAD/rilq_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":"0a74f9fd1675c6e9","mcp_get_code":{"code_sha256":"0a74f9fd1675c6e9"}},{"arxiv_id":"2411.11706","paper":"/paper/mc-llava-multi-concept-personalized-vision","title":"MC-LLaVA: Multi-Concept Personalized Vision-Language Model","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arctanxarc/mc-llava","path":"train/train_joint.py","file_url":"https://github.com/arctanxarc/mc-llava/blob/HEAD/train/train_joint.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8df6b1ef1f7b6704","mcp_get_code":{"code_sha256":"8df6b1ef1f7b6704"}},{"arxiv_id":"2411.06959","paper":"/paper/enat-rethinking-spatial-temporal-interactions","title":"ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/enat","path":"taming/models/logging.py","file_url":"https://github.com/leaplabthu/enat/blob/HEAD/taming/models/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2411.05735","paper":"/paper/aioli-a-unified-optimization-framework-for","title":"Aioli: A Unified Optimization Framework for Language Model Data Mixing","date":"2024-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HazyResearch/aioli","path":"utils.py","file_url":"https://github.com/HazyResearch/aioli/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":"0cb39a42612d17ad","mcp_get_code":{"code_sha256":"0cb39a42612d17ad"}},{"arxiv_id":"2411.03471","paper":"/paper/metrex-a-benchmark-for-verilog-code-metric","title":"MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scale-lab/MetRex","path":"src/utils.py","file_url":"https://github.com/scale-lab/MetRex/blob/HEAD/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":"4804099ebf1f246c","mcp_get_code":{"code_sha256":"4804099ebf1f246c"}},{"arxiv_id":"2411.01157","paper":"/paper/negative-free-self-supervised-gaussian","title":"Negative-Free Self-Supervised Gaussian Embedding of Graphs","date":"2024-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cloudy1225/SSGE","path":"utils.py","file_url":"https://github.com/Cloudy1225/SSGE/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":"d15ddbe73962ac64","mcp_get_code":{"code_sha256":"d15ddbe73962ac64"}},{"arxiv_id":"2411.00622","paper":"/paper/lingma-swe-gpt-an-open-development-process","title":"Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LingmaTongyi/Lingma-SWE-GPT","path":"app/log.py","file_url":"https://github.com/LingmaTongyi/Lingma-SWE-GPT/blob/HEAD/app/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48d86997cc5e93eb","mcp_get_code":{"code_sha256":"48d86997cc5e93eb"}},{"arxiv_id":"2410.24219","paper":"/paper/enhancing-motion-in-text-to-video-generation","title":"Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pr-ryan/demo","path":"utils/logging.py","file_url":"https://github.com/pr-ryan/demo/blob/HEAD/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"2410.24075","paper":"/paper/identifying-spatio-temporal-drivers-of","title":"Identifying Spatio-Temporal Drivers of Extreme Events","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HakamShams/Synthetic_Multivariate_Anomalies","path":"src/utils.py","file_url":"https://github.com/HakamShams/Synthetic_Multivariate_Anomalies/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":"817f4584131e12c5","mcp_get_code":{"code_sha256":"817f4584131e12c5"}},{"arxiv_id":"2410.23828","paper":"/paper/show-me-what-and-where-has-changed-question","title":"Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"like413/vista","path":"VisTA/utils/logger.py","file_url":"https://github.com/like413/vista/blob/HEAD/VisTA/utils/logger.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2410.23413","paper":"/paper/echofm-foundation-model-for-generalizable","title":"EchoFM: Foundation Model for Generalizable Echocardiogram Analysis","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sekeunkim/echofm","path":"EchoFM/util/logging.py","file_url":"https://github.com/sekeunkim/echofm/blob/HEAD/EchoFM/util/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"2410.22770","paper":"/paper/injecguard-benchmarking-and-mitigating-over","title":"InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leolee99/injecguard","path":"util.py","file_url":"https://github.com/leolee99/injecguard/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":"cb0a355b0a1f9d4a","mcp_get_code":{"code_sha256":"cb0a355b0a1f9d4a"}},{"arxiv_id":"2410.21795","paper":"/paper/robot-policy-learning-with-temporal-optimal","title":"Robot Policy Learning with Temporal Optimal Transport Reward","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fuyw/TemporalOT","path":"utils/train_utils.py","file_url":"https://github.com/fuyw/TemporalOT/blob/HEAD/utils/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"416f5fe4b92750f5","mcp_get_code":{"code_sha256":"416f5fe4b92750f5"}},{"arxiv_id":"2410.20660","paper":"/paper/turbohopp-accelerated-molecule-scaffold","title":"TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orgw/TurboHopp","path":"utils/_util_consistency.py","file_url":"https://github.com/orgw/TurboHopp/blob/HEAD/utils/_util_consistency.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80a6b97900b7f8df","mcp_get_code":{"code_sha256":"80a6b97900b7f8df"}},{"arxiv_id":"2410.19538","paper":"/paper/utilizing-image-transforms-and-diffusion","title":"Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series","date":"2024-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azencot-group/ImagenTime","path":"models/testing_models/s4.py","file_url":"https://github.com/azencot-group/ImagenTime/blob/HEAD/models/testing_models/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2410.19213","paper":"/paper/prototypical-hash-encoding-for-on-the-fly","title":"Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaiyangZheng/PHE","path":"utils/train_utils.py","file_url":"https://github.com/HaiyangZheng/PHE/blob/HEAD/utils/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"23bae118f9d2544c","mcp_get_code":{"code_sha256":"23bae118f9d2544c"}},{"arxiv_id":"2410.16954","paper":"/paper/lora-c-parameter-efficient-fine-tuning-of","title":"LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices","date":null,"month_inferred_from_arxiv_id":"2024-10","title_source":"archive","repo":"alexyyds2024/lora-C","path":"train_lora.py","file_url":"https://github.com/alexyyds2024/lora-C/blob/HEAD/train_lora.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":"9e7e00a8663387cb","mcp_get_code":{"code_sha256":"9e7e00a8663387cb"}},{"arxiv_id":"2410.16848","paper":"/paper/ethic-evaluating-large-language-models-on","title":"ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage","date":"2024-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmis-lab/ethic","path":"utils.py","file_url":"https://github.com/dmis-lab/ethic/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":"2efa413ae1891c65","mcp_get_code":{"code_sha256":"2efa413ae1891c65"}},{"arxiv_id":"2410.14488","paper":"/paper/ant-adaptive-noise-schedule-for-time-series","title":"ANT: Adaptive Noise Schedule for Time Series Diffusion Models","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seunghan96/ANT","path":"src/ANT/arch/s4.py","file_url":"https://github.com/seunghan96/ANT/blob/HEAD/src/ANT/arch/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2410.14157","paper":"/paper/beyond-autoregression-discrete-diffusion-for","title":"Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUNLP/diffusion-vs-ar","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/HKUNLP/diffusion-vs-ar/blob/HEAD/src/llmtuner/extras/logging.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":"56faa444d1b260c6","mcp_get_code":{"code_sha256":"56faa444d1b260c6"}},{"arxiv_id":"2410.14067","paper":"/paper/provable-benefits-of-complex","title":"Provable Benefits of Complex Parameterizations for Structured State Space Models","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edenlum/ssmcomplexparambenefits","path":"s4.py","file_url":"https://github.com/edenlum/ssmcomplexparambenefits/blob/HEAD/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2410.13643","paper":"/paper/fine-tuning-discrete-diffusion-models-via","title":"Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenyuwang-monica/drakes","path":"drakes_dna/utils.py","file_url":"https://github.com/chenyuwang-monica/drakes/blob/HEAD/drakes_dna/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60dbd956810fafb6","mcp_get_code":{"code_sha256":"60dbd956810fafb6"}},{"arxiv_id":"2410.10524","paper":"/paper/get-rid-of-task-isolation-a-continuous-multi","title":"Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DILab-USTCSZ/CMuST","path":"utils/logging.py","file_url":"https://github.com/DILab-USTCSZ/CMuST/blob/HEAD/utils/logging.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":"f651f9272fd5a62a","mcp_get_code":{"code_sha256":"f651f9272fd5a62a"}},{"arxiv_id":"2410.07794","paper":"/paper/simulating-images-of-radio-galaxies-with","title":"Simulating images of radio galaxies with diffusion models","date":null,"month_inferred_from_arxiv_id":"2024-10","title_source":"archive","repo":"tmartinezML/LOFAR-Diffusion","path":"src/utils/logging.py","file_url":"https://github.com/tmartinezML/LOFAR-Diffusion/blob/HEAD/src/utils/logging.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":"b4720eb5bbc00d40","mcp_get_code":{"code_sha256":"b4720eb5bbc00d40"}},{"arxiv_id":"2410.07672","paper":"/paper/macpo-weak-to-strong-alignment-via-multi","title":"MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"youganglyu/MACPO","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/youganglyu/MACPO/blob/HEAD/src/llmtuner/extras/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4049a2f89b11dd2f","mcp_get_code":{"code_sha256":"4049a2f89b11dd2f"}},{"arxiv_id":"2410.04223","paper":"/paper/multimodal-large-language-models-for-inverse","title":"Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning","date":"2024-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liugangcode/Llamole","path":"src/extras/logging.py","file_url":"https://github.com/liugangcode/Llamole/blob/HEAD/src/extras/logging.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":"4049a2f89b11dd2f","mcp_get_code":{"code_sha256":"4049a2f89b11dd2f"}},{"arxiv_id":"2410.03341","paper":"/paper/zero-shot-fact-verification-via-natural-logic","title":"Zero-Shot Fact Verification via Natural Logic and Large Language Models","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marekstrong/Zero-NatVer","path":"log_config.py","file_url":"https://github.com/marekstrong/Zero-NatVer/blob/HEAD/log_config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"8d892a74ab9fcd4a","mcp_get_code":{"code_sha256":"8d892a74ab9fcd4a"}},{"arxiv_id":"2410.01679","paper":"/paper/vineppo-unlocking-rl-potential-for-llm","title":"VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcgill-nlp/vineppo","path":"src/treetune/logging_utils.py","file_url":"https://github.com/mcgill-nlp/vineppo/blob/HEAD/src/treetune/logging_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e635b6a7bc9b6a4f","mcp_get_code":{"code_sha256":"e635b6a7bc9b6a4f"}},{"arxiv_id":"2410.01334","paper":"/paper/unveiling-language-skills-under-circuits","title":"Unveiling Language Skills via Path-Level Circuit Discovery","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zodiark-ch/language-skill-of-llms","path":"language_skill_icl.py","file_url":"https://github.com/zodiark-ch/language-skill-of-llms/blob/HEAD/language_skill_icl.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a2e2165007b7960e","mcp_get_code":{"code_sha256":"a2e2165007b7960e"}},{"arxiv_id":"2410.00373","paper":"/paper/robust-traffic-forecasting-against-spatial","title":"Robust Traffic Forecasting against Spatial Shift over Years","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dreamzz5/st-expert","path":"src/utils/logging.py","file_url":"https://github.com/dreamzz5/st-expert/blob/HEAD/src/utils/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f651f9272fd5a62a","mcp_get_code":{"code_sha256":"f651f9272fd5a62a"}},{"arxiv_id":"2410.00320","paper":"/paper/pointad-comprehending-3d-anomalies-from","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/PointAD","path":"logger.py","file_url":"https://github.com/zqhang/PointAD/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"01e8c2f30dcaa3d3","mcp_get_code":{"code_sha256":"01e8c2f30dcaa3d3"}},{"arxiv_id":"2409.17808","paper":"/paper/generative-modeling-of-molecular-dynamics","title":"Generative Modeling of Molecular Dynamics Trajectories","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bjing2016/mdgen","path":"mdgen/logger.py","file_url":"https://github.com/bjing2016/mdgen/blob/HEAD/mdgen/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba42bcc0464044e7","mcp_get_code":{"code_sha256":"ba42bcc0464044e7"}},{"arxiv_id":"2409.17265","paper":"/paper/codonmpnn-for-organism-specific-and-codon","title":"CodonMPNN for Organism Specific and Codon Optimal Inverse Folding","date":"2024-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hannesstark/codonmpnn","path":"codon/utils/logging.py","file_url":"https://github.com/hannesstark/codonmpnn/blob/HEAD/codon/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec9a324c17c1d5ce","mcp_get_code":{"code_sha256":"ec9a324c17c1d5ce"}},{"arxiv_id":"2409.06190","paper":"/paper/multi-source-music-generation-with-latent","title":"Multi-Source Music Generation with Latent Diffusion","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xzwy/msldm","path":"msldm/main/utils.py","file_url":"https://github.com/xzwy/msldm/blob/HEAD/msldm/main/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab52a777f7ea129a","mcp_get_code":{"code_sha256":"ab52a777f7ea129a"}},{"arxiv_id":"2409.02135","paper":"/paper/optimization-by-parallel-quasi-quantum","title":"Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling","date":"2024-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yuma-Ichikawa/QQA4CO","path":"src/qqa/_logging.py","file_url":"https://github.com/Yuma-Ichikawa/QQA4CO/blob/HEAD/src/qqa/_logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"0534c34513dd9360","mcp_get_code":{"code_sha256":"0534c34513dd9360"}},{"arxiv_id":"2408.16543","paper":"/paper/statistical-and-geometrical-properties-of","title":"Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clementinechazal/KKL-divergence-gradient-flows","path":"Regularized/kale_flow_master/kernel_wasserstein_flows/kernel_wasserstein_flows/config.py","file_url":"https://github.com/clementinechazal/KKL-divergence-gradient-flows/blob/HEAD/Regularized/kale_flow_master/kernel_wasserstein_flows/kernel_wasserstein_flows/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0d3e1933eb6c0cc","mcp_get_code":{"code_sha256":"e0d3e1933eb6c0cc"}},{"arxiv_id":"2408.05087","paper":"/paper/bootstrap-latents-of-nodes-and-neighbors-for","title":"Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning","date":"2024-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cloudy1225/blnn","path":"bgrl/utils.py","file_url":"https://github.com/cloudy1225/blnn/blob/HEAD/bgrl/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":"d15ddbe73962ac64","mcp_get_code":{"code_sha256":"d15ddbe73962ac64"}},{"arxiv_id":"2408.03765","paper":"/paper/reliable-node-similarity-matrix-guided","title":"Reliable Node Similarity Matrix Guided Contrastive Graph Clustering","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cloudy1225/NS4GC","path":"utils.py","file_url":"https://github.com/Cloudy1225/NS4GC/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":"d15ddbe73962ac64","mcp_get_code":{"code_sha256":"d15ddbe73962ac64"}},{"arxiv_id":"2408.03675","paper":"/paper/nacl-a-general-and-effective-kv-cache","title":"NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaddlePaddle/Research","path":"CV/SMILE/finetune.py","file_url":"https://github.com/PaddlePaddle/Research/blob/HEAD/CV/SMILE/finetune.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":"833ccd510fac73d0","mcp_get_code":{"code_sha256":"833ccd510fac73d0"}},{"arxiv_id":"2407.21489","paper":"/paper/2407-21489","title":"Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SapienzaNLP/maverick-coref","path":"maverick/utils/logging.py","file_url":"https://github.com/SapienzaNLP/maverick-coref/blob/HEAD/maverick/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"00efd68da46bd738","mcp_get_code":{"code_sha256":"00efd68da46bd738"}},{"arxiv_id":"2407.16674","paper":"/paper/kan-or-mlp-a-fairer-comparison","title":"KAN or MLP: A Fairer Comparison","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yu-rp/kanbefair","path":"src/utils.py","file_url":"https://github.com/yu-rp/kanbefair/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":"9d40926ce9e140ac","mcp_get_code":{"code_sha256":"9d40926ce9e140ac"}},{"arxiv_id":"2407.13998","paper":"/paper/rag-qa-arena-evaluating-domain-robustness-for","title":"RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awslabs/rag-qa-arena","path":"code/utils.py","file_url":"https://github.com/awslabs/rag-qa-arena/blob/HEAD/code/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":"38650e18e50b9afd","mcp_get_code":{"code_sha256":"38650e18e50b9afd"}},{"arxiv_id":"2407.11668","paper":"/paper/learning-to-make-keypoints-sub-pixel-accurate","title":"Learning to Make Keypoints Sub-Pixel Accurate","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kimsinjeong/keypt2subpx","path":"settings.py","file_url":"https://github.com/kimsinjeong/keypt2subpx/blob/HEAD/settings.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":"1850b68643e0cea6","mcp_get_code":{"code_sha256":"1850b68643e0cea6"}},{"arxiv_id":"2407.11569","paper":"/paper/sfpnet-sparse-focal-point-network-for","title":"SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cavendish518/SFPNet","path":"util/logger.py","file_url":"https://github.com/Cavendish518/SFPNet/blob/HEAD/util/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e985f384ed063aef","mcp_get_code":{"code_sha256":"e985f384ed063aef"}},{"arxiv_id":"2407.08751","paper":"/paper/latent-diffusion-for-neural-spiking-data","title":"Latent Diffusion for Neural Spiking Data","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mackelab/LDNS","path":"ldns/networks/blocks.py","file_url":"https://github.com/mackelab/LDNS/blob/HEAD/ldns/networks/blocks.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":"0fa25f334c0ad357","mcp_get_code":{"code_sha256":"0fa25f334c0ad357"}},{"arxiv_id":"2406.20094","paper":"/paper/scaling-synthetic-data-creation-with","title":"Scaling Synthetic Data Creation with 1,000,000,000 Personas","date":"2024-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lightaime/camel","path":"camel/logger.py","file_url":"https://github.com/lightaime/camel/blob/HEAD/camel/logger.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":"367b8f091b18560b","mcp_get_code":{"code_sha256":"367b8f091b18560b"}},{"arxiv_id":"2406.17815","paper":"/paper/sum-saliency-unification-through-mamba-for","title":"SUM: Saliency Unification through Mamba for Visual Attention Modeling","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Arhosseini77/SUM","path":"net/utils.py","file_url":"https://github.com/Arhosseini77/SUM/blob/HEAD/net/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2406.15786","paper":"/paper/what-matters-in-transformers-not-all","title":"What Matters in Transformers? Not All Attention is Needed","date":"2024-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"case-lab-umd/llm-drop","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/case-lab-umd/llm-drop/blob/HEAD/src/llmtuner/extras/logging.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":"4049a2f89b11dd2f","mcp_get_code":{"code_sha256":"4049a2f89b11dd2f"}},{"arxiv_id":"2406.13770","paper":"/paper/elliptical-attention","title":"Elliptical Attention","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stefvk/Elliptical-Attention","path":"ImageNet/logger.py","file_url":"https://github.com/stefvk/Elliptical-Attention/blob/HEAD/ImageNet/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d142e8a68b535885","mcp_get_code":{"code_sha256":"d142e8a68b535885"}},{"arxiv_id":"2406.05531","paper":"/paper/enhancing-adversarial-transferability-via","title":"Enhancing Adversarial Transferability via Information Bottleneck Constraints","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biqing-qi/enhancing-adversarial-transferability-via-information-bottleneck-constraints","path":"load_models.py","file_url":"https://github.com/biqing-qi/enhancing-adversarial-transferability-via-information-bottleneck-constraints/blob/HEAD/load_models.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2406.05478","paper":"/paper/revisiting-non-autoregressive-transformers","title":"Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/improvednat","path":"taming/models/logging.py","file_url":"https://github.com/leaplabthu/improvednat/blob/HEAD/taming/models/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"376bf22d5737fc2a","mcp_get_code":{"code_sha256":"376bf22d5737fc2a"}},{"arxiv_id":"2406.03386","paper":"/paper/learning-long-range-dependencies-on-graphs","title":"Learning Long Range Dependencies on Graphs via Random Walks","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"borgwardtlab/neuralwalker","path":"neuralwalker/modules/s4.py","file_url":"https://github.com/borgwardtlab/neuralwalker/blob/HEAD/neuralwalker/modules/s4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2405.19266","paper":"/paper/pediatricsgpt-large-language-models-as","title":"PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ydk122024/PediatricsGPT","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/ydk122024/PediatricsGPT/blob/HEAD/src/llmtuner/extras/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4049a2f89b11dd2f","mcp_get_code":{"code_sha256":"4049a2f89b11dd2f"}},{"arxiv_id":"2405.17898","paper":"/paper/flashst-a-simple-and-universal-prompt-tuning","title":"FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUDS/FlashST","path":"lib/logger.py","file_url":"https://github.com/HKUDS/FlashST/blob/HEAD/lib/logger.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":"996764623af1eab9","mcp_get_code":{"code_sha256":"996764623af1eab9"}},{"arxiv_id":"2405.17264","paper":"/paper/on-the-noise-robustness-of-in-context","title":"On the Noise Robustness of In-Context Learning for Text Generation","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-stat-Sustech/Local-Perplexity-Ranking","path":"common/logging.py","file_url":"https://github.com/ml-stat-Sustech/Local-Perplexity-Ranking/blob/HEAD/common/logging.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":"792a2c8b12b13057","mcp_get_code":{"code_sha256":"792a2c8b12b13057"}},{"arxiv_id":"2405.16273","paper":"/paper/m-3-gpt-an-advanced-multimodal-multitask","title":"M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luomingshuang/M3GPT","path":"m3gpt/utils.py","file_url":"https://github.com/luomingshuang/M3GPT/blob/HEAD/m3gpt/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd1cba25bacbf54d","mcp_get_code":{"code_sha256":"bd1cba25bacbf54d"}},{"arxiv_id":"2405.15463","paper":"/paper/pointramba-a-hybrid-transformer-mamba","title":"PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoyao3302/pointramba","path":"part_segmentation/logger.py","file_url":"https://github.com/xiaoyao3302/pointramba/blob/HEAD/part_segmentation/logger.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":"13248d61d84b9cee","mcp_get_code":{"code_sha256":"13248d61d84b9cee"}},{"arxiv_id":"2405.14325","paper":"/paper/dinomaly-the-less-is-more-philosophy-in-multi","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guojiajeremy/dinomaly","path":"dinomaly_mpdd_sep.py","file_url":"https://github.com/guojiajeremy/dinomaly/blob/HEAD/dinomaly_mpdd_sep.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":"a1db6c55cfde6e19","mcp_get_code":{"code_sha256":"a1db6c55cfde6e19"}},{"arxiv_id":"2405.05695","paper":"/paper/aux-nas-exploiting-auxiliary-labels-with","title":"Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanygao/Aux-NAS","path":"utils/logger.py","file_url":"https://github.com/ethanygao/Aux-NAS/blob/HEAD/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8346d5d38e5f0042","mcp_get_code":{"code_sha256":"8346d5d38e5f0042"}},{"arxiv_id":"2405.04517","paper":"/paper/xlstm-extended-long-short-term-memory","title":"xLSTM: Extended Long Short-Term Memory","date":"2024-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gonzalopezgil/xlstm-ts","path":"src/ml/utils/logger.py","file_url":"https://github.com/gonzalopezgil/xlstm-ts/blob/HEAD/src/ml/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a707ddedc88e249","mcp_get_code":{"code_sha256":"5a707ddedc88e249"}},{"arxiv_id":"2404.16969","paper":"/paper/cocola-coherence-oriented-contrastive","title":"COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations","date":"2024-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emilianpostolache/stable-audio-controlnet","path":"main/utils.py","file_url":"https://github.com/emilianpostolache/stable-audio-controlnet/blob/HEAD/main/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab52a777f7ea129a","mcp_get_code":{"code_sha256":"ab52a777f7ea129a"}},{"arxiv_id":"2404.12699","paper":"/paper/sophon-non-fine-tunable-learning-to-restrain","title":"SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chiange/sophon","path":"generation/utils.py","file_url":"https://github.com/chiange/sophon/blob/HEAD/generation/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7afc765004593554","mcp_get_code":{"code_sha256":"7afc765004593554"}},{"arxiv_id":"2404.09498","paper":"/paper/fusionmamba-dynamic-feature-enhancement-for","title":"FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"milliexie/fusionmamba","path":"utils.py","file_url":"https://github.com/milliexie/fusionmamba/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2404.09387","paper":"/paper/rankclip-ranking-consistent-language-image","title":"RankCLIP: Ranking-Consistent Language-Image Pretraining","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jam1ezhang/rankclip","path":"rankclip/logger.py","file_url":"https://github.com/jam1ezhang/rankclip/blob/HEAD/rankclip/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"75f67d27c89765cd","mcp_get_code":{"code_sha256":"75f67d27c89765cd"}},{"arxiv_id":"2404.06480","paper":"/paper/ada-leval-evaluating-long-context-llms-with","title":"Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"open-compass/ada-leval","path":"ada_leval/util.py","file_url":"https://github.com/open-compass/ada-leval/blob/HEAD/ada_leval/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a50476e2d7c30009","mcp_get_code":{"code_sha256":"a50476e2d7c30009"}},{"arxiv_id":"2404.06065","paper":"/paper/unified-entropy-optimization-for-open-set","title":"Unified Entropy Optimization for Open-Set Test-Time Adaptation","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gaozhengqing/UniEnt","path":"imagenet/utils.py","file_url":"https://github.com/gaozhengqing/UniEnt/blob/HEAD/imagenet/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"211f0b5c566ca621","mcp_get_code":{"code_sha256":"211f0b5c566ca621"}},{"arxiv_id":"2404.05163","paper":"/paper/semantic-flow-learning-semantic-field-of","title":"Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfr/semantic-flow","path":"train/train_ddp_sceneflow_fast_rendering_semantic.py","file_url":"https://github.com/tianfr/semantic-flow/blob/HEAD/train/train_ddp_sceneflow_fast_rendering_semantic.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":"a8a40ec445566b9b","mcp_get_code":{"code_sha256":"a8a40ec445566b9b"}},{"arxiv_id":"2403.20035","paper":"/paper/ultralight-vm-unet-parallel-vision-mamba","title":"UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wurenkai/UltraLight-VM-UNet","path":"utils.py","file_url":"https://github.com/wurenkai/UltraLight-VM-UNet/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2403.18802","paper":"/paper/long-form-factuality-in-large-language-models","title":"Long-form factuality in large language models","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chandralegend/saf-eval","path":"saf_eval/utils/logging.py","file_url":"https://github.com/chandralegend/saf-eval/blob/HEAD/saf_eval/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"297aaefc12e89e13","mcp_get_code":{"code_sha256":"297aaefc12e89e13"}},{"arxiv_id":"2403.13786","paper":"/paper/chain-of-interaction-enhancing-large-language","title":"Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trust-nlp/coi-psychotherapy","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/trust-nlp/coi-psychotherapy/blob/HEAD/src/llmtuner/extras/logging.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":"0905fc3e8ed7fea3","mcp_get_code":{"code_sha256":"0905fc3e8ed7fea3"}},{"arxiv_id":"2403.13642","paper":"/paper/h-vmunet-high-order-vision-mamba-unet-for","title":"H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wurenkai/h-vmunet","path":"utils.py","file_url":"https://github.com/wurenkai/h-vmunet/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2403.04732","paper":"/paper/how-far-are-we-from-intelligent-visual","title":"How Far Are We from Intelligent Visual Deductive Reasoning?","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-rpm-bench","path":"src/utils.py","file_url":"https://github.com/apple/ml-rpm-bench/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":"75de6fc5f7feb96b","mcp_get_code":{"code_sha256":"75de6fc5f7feb96b"}},{"arxiv_id":"2403.02683","paper":"/paper/learning-to-defer-to-a-population-a-meta","title":"Learning to Defer to a Population: A Meta-Learning Approach","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvtailor/meta-l2d","path":"lib/utils.py","file_url":"https://github.com/dvtailor/meta-l2d/blob/HEAD/lib/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f4965e3468a2cd0","mcp_get_code":{"code_sha256":"2f4965e3468a2cd0"}},{"arxiv_id":"2402.17287","paper":"/paper/an-interpretable-evaluation-of-entropy-based","title":"An Interpretable Evaluation of Entropy-based Novelty of Generative Models","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buyeah1109/KEN","path":"KEN/metric/KEN.py","file_url":"https://github.com/buyeah1109/KEN/blob/HEAD/KEN/metric/KEN.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":"6fe0f876914b5bfe","mcp_get_code":{"code_sha256":"6fe0f876914b5bfe"}},{"arxiv_id":"2402.14905","paper":"/paper/mobilellm-optimizing-sub-billion-parameter","title":"MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/MobileLLM-R1","path":"pretrain/pretrain.py","file_url":"https://github.com/facebookresearch/MobileLLM-R1/blob/HEAD/pretrain/pretrain.py","status":"unverified","verification_level":0,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e3820b27e772b468","mcp_get_code":{"code_sha256":"e3820b27e772b468"}},{"arxiv_id":"2402.11863","paper":"/paper/how-interpretable-are-reasoning-explanations","title":"How Interpretable are Reasoning Explanations from Prompting Large Language Models?","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wj210/cot_interpretability","path":"utils/utils.py","file_url":"https://github.com/wj210/cot_interpretability/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91902d78bfbae499","mcp_get_code":{"code_sha256":"91902d78bfbae499"}},{"arxiv_id":"2402.11722","paper":"/paper/invertible-fourier-neural-operators-for","title":"Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bayesianaigroup/ifno","path":"logger.py","file_url":"https://github.com/bayesianaigroup/ifno/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5d1c3dff0373b23a","mcp_get_code":{"code_sha256":"5d1c3dff0373b23a"}},{"arxiv_id":"2402.11235","paper":"/paper/zerog-investigating-cross-dataset-zero-shot","title":"ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nineabyss/zerog","path":"code/main_TextBP_benchmark.py","file_url":"https://github.com/nineabyss/zerog/blob/HEAD/code/main_TextBP_benchmark.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":"7ea4e74aa633c5d3","mcp_get_code":{"code_sha256":"7ea4e74aa633c5d3"}},{"arxiv_id":"2402.10635","paper":"/paper/contiformer-continuous-time-transformer-for-1","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","date":"2024-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/SeqML","path":"ContiFormer/spiral.py","file_url":"https://github.com/microsoft/SeqML/blob/HEAD/ContiFormer/spiral.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc2478e15ade0979","mcp_get_code":{"code_sha256":"dc2478e15ade0979"}},{"arxiv_id":"2402.06087","paper":"/paper/descriptive-kernel-convolution-network-with","title":"Descriptive Kernel Convolution Network with Improved Random Walk Kernel","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mengchillee/rwk_plus","path":"sec_5_1_RWKP/graph_classification/utils.py","file_url":"https://github.com/mengchillee/rwk_plus/blob/HEAD/sec_5_1_RWKP/graph_classification/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e404fe5eeda6aba4","mcp_get_code":{"code_sha256":"e404fe5eeda6aba4"}},{"arxiv_id":"2402.04875","paper":"/paper/on-provable-length-and-compositional","title":"On Provable Length and Compositional Generalization","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/length-and-compositional-generalization","path":"src/utils/general.py","file_url":"https://github.com/facebookresearch/length-and-compositional-generalization/blob/HEAD/src/utils/general.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2402.04845","paper":"/paper/alphafold-meets-flow-matching-for-generating","title":"AlphaFold Meets Flow Matching for Generating Protein Ensembles","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bjing2016/alphaflow","path":"alphaflow/utils/logging.py","file_url":"https://github.com/bjing2016/alphaflow/blob/HEAD/alphaflow/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba42bcc0464044e7","mcp_get_code":{"code_sha256":"ba42bcc0464044e7"}},{"arxiv_id":"2402.03720","paper":"/paper/similarity-based-neighbor-selection-for-graph","title":"Similarity-based Neighbor Selection for Graph LLMs","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruili33/sns","path":"logger.py","file_url":"https://github.com/ruili33/sns/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ed790f0d6b309cb","mcp_get_code":{"code_sha256":"0ed790f0d6b309cb"}},{"arxiv_id":"2402.03660","paper":"/paper/cross-task-linearity-emerges-in-the","title":"On the Emergence of Cross-Task Linearity in the Pretraining-Finetuning Paradigm","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzp1012/cross-task-linearity","path":"task_arithemetic_t5/utils.py","file_url":"https://github.com/zzp1012/cross-task-linearity/blob/HEAD/task_arithemetic_t5/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edd9c782220bb4e8","mcp_get_code":{"code_sha256":"edd9c782220bb4e8"}},{"arxiv_id":"2402.02491","paper":"/paper/vm-unet-vision-mamba-unet-for-medical-image","title":"VM-UNet: Vision Mamba UNet for Medical Image Segmentation","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcruan519/vm-unet","path":"utils.py","file_url":"https://github.com/jcruan519/vm-unet/blob/HEAD/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2402.02446","paper":"/paper/lqer-low-rank-quantization-error","title":"LQER: Low-Rank Quantization Error Reconstruction for LLMs","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chengzhang-98/lqer","path":"src/lqer/logging.py","file_url":"https://github.com/chengzhang-98/lqer/blob/HEAD/src/lqer/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6221863a7f7985b2","mcp_get_code":{"code_sha256":"6221863a7f7985b2"}},{"arxiv_id":"2402.00411","paper":"/paper/lm-ht-snn-enhancing-the-performance-of-snn-to","title":"LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzc1208/lmht_snn","path":"hybrid_train.py","file_url":"https://github.com/hzc1208/lmht_snn/blob/HEAD/hybrid_train.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2402.18583","paper":"/paper/binding-adaptive-diffusion-models-for","title":"Binding-Adaptive Diffusion Models for Structure-Based Drug Design","date":"2024-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YangLing0818/BindDM","path":"utils/misc.py","file_url":"https://github.com/YangLing0818/BindDM/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e85bd811721f0513","mcp_get_code":{"code_sha256":"e85bd811721f0513"}},{"arxiv_id":"2402.00159","paper":"/paper/dolma-an-open-corpus-of-three-trillion-tokens","title":"Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research","date":"2024-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/dolma","path":"python/dolma/core/loggers.py","file_url":"https://github.com/allenai/dolma/blob/HEAD/python/dolma/core/loggers.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":"13c0d6acf12fe992","mcp_get_code":{"code_sha256":"13c0d6acf12fe992"}},{"arxiv_id":"2401.16745","paper":"/paper/mt-eval-a-multi-turn-capabilities-evaluation","title":"MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models","date":"2024-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kwanwaichung/mt-eval","path":"utils/misc.py","file_url":"https://github.com/kwanwaichung/mt-eval/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"506bf38508c1c41e","mcp_get_code":{"code_sha256":"506bf38508c1c41e"}},{"arxiv_id":"2401.10491","paper":"/paper/knowledge-fusion-of-large-language-models","title":"Knowledge Fusion of Large Language Models","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanqiwan/FuseLLM","path":"FuseLLM/src/utils/others.py","file_url":"https://github.com/fanqiwan/FuseLLM/blob/HEAD/FuseLLM/src/utils/others.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1082508e65f77331","mcp_get_code":{"code_sha256":"1082508e65f77331"}},{"arxiv_id":"2401.10166","paper":"/paper/vmamba-visual-state-space-model","title":"VMamba: Visual State Space Model","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zs1314/skinmamba","path":"utils.py","file_url":"https://github.com/zs1314/skinmamba/blob/HEAD/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2401.06442","paper":"/paper/rotationdrag-point-based-image-editing-with","title":"RotationDrag: Point-based Image Editing with Rotated Diffusion Features","date":"2024-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony-lowe/rotationdrag","path":"utils/logger.py","file_url":"https://github.com/tony-lowe/rotationdrag/blob/HEAD/utils/logger.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":"0e22527aef7473f7","mcp_get_code":{"code_sha256":"0e22527aef7473f7"}},{"arxiv_id":"2401.05584","paper":"/paper/fourcastnext-improving-fourcastnet-training","title":"FourCastNeXt: Optimizing FourCastNet Training for Limited Compute","date":"2024-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nci/fourcastnext","path":"utils/util.py","file_url":"https://github.com/nci/fourcastnext/blob/HEAD/utils/util.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":"856f467c34d19c2d","mcp_get_code":{"code_sha256":"856f467c34d19c2d"}},{"arxiv_id":"2401.04829","paper":"/paper/gnnshap-fast-and-accurate-gnn-explanations","title":"GNNShap: Scalable and Accurate GNN Explanation using Shapley Values","date":"2024-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HipGraph/GNNShap","path":"gnnshap/utils.py","file_url":"https://github.com/HipGraph/GNNShap/blob/HEAD/gnnshap/utils.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":"f57e4ba066540792","mcp_get_code":{"code_sha256":"f57e4ba066540792"}},{"arxiv_id":"2401.03006","paper":"/paper/the-rise-of-diffusion-models-in-time-series","title":"The Rise of Diffusion Models in Time-Series Forecasting","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4healthuol/sssd","path":"src/imputers/CSDIS4.py","file_url":"https://github.com/ai4healthuol/sssd/blob/HEAD/src/imputers/CSDIS4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2401.01578","paper":"/paper/context-guided-spatio-temporal-video","title":"Context-Guided Spatio-Temporal Video Grounding","date":"2024-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shaohuadong2021/dplnet","path":"RGBD/toolbox/log.py","file_url":"https://github.com/shaohuadong2021/dplnet/blob/HEAD/RGBD/toolbox/log.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"229c212d6572dfe1","mcp_get_code":{"code_sha256":"229c212d6572dfe1"}},{"arxiv_id":"2312.16245","paper":"/paper/ikun-speak-to-trackers-without-retraining","title":"iKUN: Speak to Trackers without Retraining","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dyhbupt/ikun","path":"utils.py","file_url":"https://github.com/dyhbupt/ikun/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":"003ac47d15830f47","mcp_get_code":{"code_sha256":"003ac47d15830f47"}},{"arxiv_id":"2312.10103","paper":"/paper/gsva-generalized-segmentation-via-multimodal","title":"GSVA: Generalized Segmentation via Multimodal Large Language Models","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/gsva","path":"utils/logger.py","file_url":"https://github.com/leaplabthu/gsva/blob/HEAD/utils/logger.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":"7e11d9ec27335f5b","mcp_get_code":{"code_sha256":"7e11d9ec27335f5b"}},{"arxiv_id":"2312.09109","paper":"/paper/videolcm-video-latent-consistency-model","title":"VideoLCM: Video Latent Consistency Model","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ali-vilab/VGen","path":"utils/logging.py","file_url":"https://github.com/ali-vilab/VGen/blob/HEAD/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"2312.08656","paper":"/paper/maxk-gnn-towards-theoretical-speed-limits-for","title":"MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"harveyp123/maxk-gnn","path":"utils/general_utils.py","file_url":"https://github.com/harveyp123/maxk-gnn/blob/HEAD/utils/general_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0fb6732d5ca48437","mcp_get_code":{"code_sha256":"0fb6732d5ca48437"}},{"arxiv_id":"2312.02554","paper":"/paper/ulma-unified-language-model-alignment-with","title":"ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unified-language-model-alignment/src","path":"src/llmtuner/extras/logging.py","file_url":"https://github.com/unified-language-model-alignment/src/blob/HEAD/src/llmtuner/extras/logging.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":"56faa444d1b260c6","mcp_get_code":{"code_sha256":"56faa444d1b260c6"}},{"arxiv_id":"2312.00752","paper":"/paper/mamba-linear-time-sequence-modeling-with","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lab-emi/cleanumamba","path":"src/network/S4/MambaS4.py","file_url":"https://github.com/lab-emi/cleanumamba/blob/HEAD/src/network/S4/MambaS4.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":"3fcbb53cc7333590","mcp_get_code":{"code_sha256":"3fcbb53cc7333590"}},{"arxiv_id":"2311.17034","paper":"/paper/telling-left-from-right-identifying-geometry","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Junyi42/geoaware-sc","path":"utils/logger.py","file_url":"https://github.com/Junyi42/geoaware-sc/blob/HEAD/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e22527aef7473f7","mcp_get_code":{"code_sha256":"0e22527aef7473f7"}},{"arxiv_id":"2311.16026","paper":"/paper/a-neural-framework-for-generalized-causal","title":"A Neural Framework for Generalized Causal Sensitivity Analysis","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dennisfrauen/neuralcsa","path":"models/stage2.py","file_url":"https://github.com/dennisfrauen/neuralcsa/blob/HEAD/models/stage2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"55301e603a0ed76d","mcp_get_code":{"code_sha256":"55301e603a0ed76d"}},{"arxiv_id":"2311.13246","paper":"/paper/automatic-instruction-optimization-for-open","title":"CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lunyiliu/coachlm","path":"src/glmtuner/extras/logging.py","file_url":"https://github.com/lunyiliu/coachlm/blob/HEAD/src/glmtuner/extras/logging.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":"56faa444d1b260c6","mcp_get_code":{"code_sha256":"56faa444d1b260c6"}},{"arxiv_id":"2311.12472","paper":"/paper/self-supervised-deconfounding-against-spatio","title":"Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction","date":"2023-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigscity/steve_code","path":"STEVE/lib/logger.py","file_url":"https://github.com/bigscity/steve_code/blob/HEAD/STEVE/lib/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7264b453ea447aee","mcp_get_code":{"code_sha256":"7264b453ea447aee"}},{"arxiv_id":"2311.07633","paper":"/paper/rethinking-and-benchmarking-predict-then","title":"Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Thinklab-SJTU/PredictiveCO-Benchmark","path":"openpto/config/utils_conf.py","file_url":"https://github.com/Thinklab-SJTU/PredictiveCO-Benchmark/blob/HEAD/openpto/config/utils_conf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5d306d2258b565c1","mcp_get_code":{"code_sha256":"5d306d2258b565c1"}},{"arxiv_id":"2311.06062","paper":"/paper/practical-membership-inference-attacks","title":"Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration","date":"2023-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-fib-lab/neurips2024_spv-mia","path":"attack/utils.py","file_url":"https://github.com/tsinghua-fib-lab/neurips2024_spv-mia/blob/HEAD/attack/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cfea06f11710d053","mcp_get_code":{"code_sha256":"cfea06f11710d053"}},{"arxiv_id":"2311.03721","paper":"/paper/climateset-a-large-scale-climate-model","title":"ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RolnickLab/ClimateSet","path":"emulator/src/core/losses.py","file_url":"https://github.com/RolnickLab/ClimateSet/blob/HEAD/emulator/src/core/losses.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2310.20030","paper":"/paper/scaling-riemannian-diffusion-models","title":"Scaling Riemannian Diffusion Models","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"louaaron/Scaling-Riemannian-Diffusion","path":"contrastive_ood/utils.py","file_url":"https://github.com/louaaron/Scaling-Riemannian-Diffusion/blob/HEAD/contrastive_ood/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ffa3c52c6ff4de2a","mcp_get_code":{"code_sha256":"ffa3c52c6ff4de2a"}},{"arxiv_id":"2310.18961","paper":"/paper/anomalyclip-object-agnostic-prompt-learning","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/AnomalyCLIP","path":"logger.py","file_url":"https://github.com/zqhang/AnomalyCLIP/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01e8c2f30dcaa3d3","mcp_get_code":{"code_sha256":"01e8c2f30dcaa3d3"}},{"arxiv_id":"2310.18349","paper":"/paper/a-boundary-offset-prediction-network-for","title":"A Boundary Offset Prediction Network for Named Entity Recognition","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mhtang1995/bopn","path":"src/utils.py","file_url":"https://github.com/mhtang1995/bopn/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":"d188dfca4ca84c79","mcp_get_code":{"code_sha256":"d188dfca4ca84c79"}},{"arxiv_id":"2310.16355","paper":"/paper/redco-a-lightweight-tool-to-automate","title":"RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tanyuqian/redco","path":"redco/deployers/log_utils.py","file_url":"https://github.com/tanyuqian/redco/blob/HEAD/redco/deployers/log_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":"1879d59e1c180941","mcp_get_code":{"code_sha256":"1879d59e1c180941"}},{"arxiv_id":"2310.11523","paper":"/paper/group-preference-optimization-few-shot","title":"Group Preference Optimization: Few-Shot Alignment of Large Language Models","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamqd/Group-Preference-Optimization","path":"utils/log.py","file_url":"https://github.com/jamqd/Group-Preference-Optimization/blob/HEAD/utils/log.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13efed1531b7955b","mcp_get_code":{"code_sha256":"13efed1531b7955b"}},{"arxiv_id":"2310.09751","paper":"/paper/unitime-a-language-empowered-unified-model","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","date":"2023-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxu77/unitime","path":"utils/logger.py","file_url":"https://github.com/liuxu77/unitime/blob/HEAD/utils/logger.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":"f651f9272fd5a62a","mcp_get_code":{"code_sha256":"f651f9272fd5a62a"}},{"arxiv_id":"2310.07402","paper":"/paper/nutime-numerically-multi-scaled-embedding-for","title":"NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenguolin/nutime","path":"src/utils/util.py","file_url":"https://github.com/chenguolin/nutime/blob/HEAD/src/utils/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2ff86ca79e1aa79","mcp_get_code":{"code_sha256":"e2ff86ca79e1aa79"}},{"arxiv_id":"2310.06368","paper":"/paper/coinseg-contrast-inter-and-intra-class-1","title":"CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkzhang98/coinseg","path":"network/swin_utils.py","file_url":"https://github.com/zkzhang98/coinseg/blob/HEAD/network/swin_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c9157ee08c3b07bf","mcp_get_code":{"code_sha256":"c9157ee08c3b07bf"}},{"arxiv_id":"2310.05862","paper":"/paper/better-safe-than-sorry-pre-training-clip","title":"Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigml-cs-ucla/safeclip","path":"SafeCLIP/src/logger.py","file_url":"https://github.com/bigml-cs-ucla/safeclip/blob/HEAD/SafeCLIP/src/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"75f67d27c89765cd","mcp_get_code":{"code_sha256":"75f67d27c89765cd"}},{"arxiv_id":"2309.14681","paper":"/paper/are-human-generated-demonstrations-necessary","title":"Are Human-generated Demonstrations Necessary for In-context Learning?","date":"2023-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruili33/sec","path":"logger.py","file_url":"https://github.com/ruili33/sec/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"724570e6d89a99ca","mcp_get_code":{"code_sha256":"724570e6d89a99ca"}},{"arxiv_id":"2309.09055","paper":"/paper/exploring-the-impact-of-low-rank-adaptation","title":"Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHF","date":"2023-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simengsun/alpaca_farm_lora","path":"alpaca_farm/src/alpaca_farm/logging.py","file_url":"https://github.com/simengsun/alpaca_farm_lora/blob/HEAD/alpaca_farm/src/alpaca_farm/logging.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":"043cf4a6fd60480b","mcp_get_code":{"code_sha256":"043cf4a6fd60480b"}},{"arxiv_id":"2308.16177","paper":"/paper/general-purpose-audio-effect-removal","title":"General Purpose Audio Effect Removal","date":null,"month_inferred_from_arxiv_id":"2023-08","title_source":"archive","repo":"mhrice/RemFx","path":"remfx/utils.py","file_url":"https://github.com/mhrice/RemFx/blob/HEAD/remfx/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":"ab52a777f7ea129a","mcp_get_code":{"code_sha256":"ab52a777f7ea129a"}},{"arxiv_id":"2308.14129","paper":"/paper/speed-streaming-partition-and-parallel","title":"SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding","date":"2023-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxi1228/SPEED","path":"train_utils.py","file_url":"https://github.com/chenxi1228/SPEED/blob/HEAD/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"06e1aa58af8f7f4a","mcp_get_code":{"code_sha256":"06e1aa58af8f7f4a"}},{"arxiv_id":"2308.08713","paper":"/paper/decoding-emotions-a-comprehensive","title":"Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition","date":"2023-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"95anantsingh/decoding-emotions","path":"utils/logger.py","file_url":"https://github.com/95anantsingh/decoding-emotions/blob/HEAD/utils/logger.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":"e23a7c4aca062560","mcp_get_code":{"code_sha256":"e23a7c4aca062560"}},{"arxiv_id":"2308.06582","paper":"/paper/gated-attention-coding-for-training-high","title":"Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks","date":"2023-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bollossom/GAC","path":"CODE/ImageNet/functions.py","file_url":"https://github.com/bollossom/GAC/blob/HEAD/CODE/ImageNet/functions.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2308.06391","paper":"/paper/dynamic-planning-with-a-llm","title":"Dynamic Planning with a LLM","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itl-ed/llm-dp","path":"utils/logger.py","file_url":"https://github.com/itl-ed/llm-dp/blob/HEAD/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"528dead7ac0f47de","mcp_get_code":{"code_sha256":"528dead7ac0f47de"}},{"arxiv_id":"2308.04008","paper":"/paper/coarse-to-fine-learning-compact","title":"Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bassyess/CFCD","path":"core/logging.py","file_url":"https://github.com/bassyess/CFCD/blob/HEAD/core/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e4e129cb77e7f8b","mcp_get_code":{"code_sha256":"8e4e129cb77e7f8b"}},{"arxiv_id":"2308.02989","paper":"/paper/novel-class-discovery-for-long-tailed","title":"Novel Class Discovery for Long-tailed Recognition","date":"2023-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kleinzcy/ncdlr","path":"utils/build.py","file_url":"https://github.com/kleinzcy/ncdlr/blob/HEAD/utils/build.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"086ad63ce4ac3db3","mcp_get_code":{"code_sha256":"086ad63ce4ac3db3"}},{"arxiv_id":"2307.11984","paper":"/paper/learning-vision-and-language-navigation-from","title":"Learning Vision-and-Language Navigation from YouTube Videos","date":"2023-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeremylinky/youtube-vln","path":"utils/misc.py","file_url":"https://github.com/jeremylinky/youtube-vln/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1efda40c7463ba10","mcp_get_code":{"code_sha256":"1efda40c7463ba10"}},{"arxiv_id":"2307.10768","paper":"/paper/decoding-the-enigma-benchmarking-humans-and","title":"Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanglab-deepneurocoglab/worm","path":"src/utils/logger.py","file_url":"https://github.com/zhanglab-deepneurocoglab/worm/blob/HEAD/src/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f04c1e45dabfc970","mcp_get_code":{"code_sha256":"f04c1e45dabfc970"}},{"arxiv_id":"2307.08473","paper":"/paper/ege-unet-an-efficient-group-enhanced-unet-for","title":"EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcruan519/ege-unet","path":"utils.py","file_url":"https://github.com/jcruan519/ege-unet/blob/HEAD/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":"23a1f34627822bb3","mcp_get_code":{"code_sha256":"23a1f34627822bb3"}},{"arxiv_id":"2307.00285","paper":"/paper/assembled-openml-creating-efficient","title":"Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML","date":"2023-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isg-siegen/assembled","path":"assembled/utils/logger.py","file_url":"https://github.com/isg-siegen/assembled/blob/HEAD/assembled/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"154a460210aaad8f","mcp_get_code":{"code_sha256":"154a460210aaad8f"}},{"arxiv_id":"2306.14451","paper":"/paper/learning-prompt-enhanced-context-features-for","title":"Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection","date":"2023-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yujiangpu20/pel4vad","path":"log.py","file_url":"https://github.com/yujiangpu20/pel4vad/blob/HEAD/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e54688f4d92cf4e6","mcp_get_code":{"code_sha256":"e54688f4d92cf4e6"}},{"arxiv_id":"2306.13856","paper":"/paper/learning-to-rank-meets-language-boosting-1","title":"Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification","date":"2023-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xk-huang/OrdinalCLIP","path":"ordinalclip/utils/logging.py","file_url":"https://github.com/xk-huang/OrdinalCLIP/blob/HEAD/ordinalclip/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"057d3a33431e9827","mcp_get_code":{"code_sha256":"057d3a33431e9827"}},{"arxiv_id":"2306.08860","paper":"/paper/oms-dpm-optimizing-the-model-schedule-for","title":"OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jsttlgdkycy/oms-dpm","path":"code/predictor/main_predictor.py","file_url":"https://github.com/jsttlgdkycy/oms-dpm/blob/HEAD/code/predictor/main_predictor.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":"8798ef07b4cea3cb","mcp_get_code":{"code_sha256":"8798ef07b4cea3cb"}},{"arxiv_id":"2306.06955","paper":"/paper/a-brief-review-of-hypernetworks-in-deep","title":"A Brief Review of Hypernetworks in Deep Learning","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jmdvinodjmd/HyperITE","path":"src/utils.py","file_url":"https://github.com/jmdvinodjmd/HyperITE/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":"c8f7fcc00a2439dc","mcp_get_code":{"code_sha256":"c8f7fcc00a2439dc"}},{"arxiv_id":"2306.06138","paper":"/paper/extraction-and-recovery-of-spatio-temporal-1","title":"Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models","date":"2023-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexwangntl/erdiff","path":"utils/utils_torch.py","file_url":"https://github.com/alexwangntl/erdiff/blob/HEAD/utils/utils_torch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c969dec60bfb5ba4","mcp_get_code":{"code_sha256":"c969dec60bfb5ba4"}},{"arxiv_id":"2306.02602","paper":"/paper/recontrast-domain-specific-anomaly-detection-1","title":"ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guojiajeremy/ReContrast","path":"recontrast_mvtec.py","file_url":"https://github.com/guojiajeremy/ReContrast/blob/HEAD/recontrast_mvtec.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":"a1db6c55cfde6e19","mcp_get_code":{"code_sha256":"a1db6c55cfde6e19"}},{"arxiv_id":"2306.02018","paper":"/paper/videocomposer-compositional-video-synthesis","title":"VideoComposer: Compositional Video Synthesis with Motion Controllability","date":"2023-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"damo-vilab/videocomposer","path":"utils/logging.py","file_url":"https://github.com/damo-vilab/videocomposer/blob/HEAD/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"2306.01981","paper":"/paper/sgem-test-time-adaptation-for-automatic","title":"SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization","date":"2023-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"drumpt/sgem","path":"utils.py","file_url":"https://github.com/drumpt/sgem/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":"d2935ad1f2de53f6","mcp_get_code":{"code_sha256":"d2935ad1f2de53f6"}},{"arxiv_id":"2306.00917","paper":"/paper/vocabulary-free-image-classification-1","title":"Vocabulary-free Image Classification","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"altndrr/vic","path":"src/utils/logging_utils.py","file_url":"https://github.com/altndrr/vic/blob/HEAD/src/utils/logging_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fca99bddc15e99cc","mcp_get_code":{"code_sha256":"fca99bddc15e99cc"}},{"arxiv_id":"2305.16988","paper":null,"title":"arXiv:2305.16988","date":null,"month_inferred_from_arxiv_id":"2023-05","title_source":null,"repo":"DennisFrauen/SharpCausalSensitivity","path":"models/gmsm.py","file_url":"https://github.com/DennisFrauen/SharpCausalSensitivity/blob/HEAD/models/gmsm.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"46b40b095115f70c","mcp_get_code":{"code_sha256":"46b40b095115f70c"}},{"arxiv_id":"2305.14516","paper":"/paper/2305-14516","title":"Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chakra-et/chakra","path":"et_converter/et_converter.py","file_url":"https://github.com/chakra-et/chakra/blob/HEAD/et_converter/et_converter.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":"215e046594661350","mcp_get_code":{"code_sha256":"215e046594661350"}},{"arxiv_id":"2305.12761","paper":"/paper/enhancing-cross-lingual-natural-language-1","title":"Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-bpm/softmv","path":"log.py","file_url":"https://github.com/thu-bpm/softmv/blob/HEAD/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"999a29a2aa943ed5","mcp_get_code":{"code_sha256":"999a29a2aa943ed5"}},{"arxiv_id":"2305.07508","paper":"/paper/moldiff-addressing-the-atom-bond","title":"MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengxingang/moldiff","path":"utils/misc.py","file_url":"https://github.com/pengxingang/moldiff/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":"faa72a1daab22e92","mcp_get_code":{"code_sha256":"faa72a1daab22e92"}},{"arxiv_id":"2305.03510","paper":"/paper/parameter-efficient-cross-lingual-transfer-of","title":"Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment","date":"2023-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eric-ai-lab/pectvlm","path":"align_from_to_en/mylogging.py","file_url":"https://github.com/eric-ai-lab/pectvlm/blob/HEAD/align_from_to_en/mylogging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be3d0f095f77385f","mcp_get_code":{"code_sha256":"be3d0f095f77385f"}},{"arxiv_id":"2305.02507","paper":"/paper/stimulative-training-go-beyond-the","title":"Stimulative Training++: Go Beyond The Performance Limits of Residual Networks","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshine-ye/nips22-st","path":"utils/setlogger.py","file_url":"https://github.com/sunshine-ye/nips22-st/blob/HEAD/utils/setlogger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84a10f5778846b16","mcp_get_code":{"code_sha256":"84a10f5778846b16"}},{"arxiv_id":"2305.01938","paper":"/paper/doc2soargraph-discrete-reasoning-over","title":"Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs","date":"2023-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengbinzhu/doc2soargraph","path":"etr/log.py","file_url":"https://github.com/fengbinzhu/doc2soargraph/blob/HEAD/etr/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85bc6fd421c42e7c","mcp_get_code":{"code_sha256":"85bc6fd421c42e7c"}},{"arxiv_id":"2304.14636","paper":"/paper/prenas-preferred-one-shot-learning-towards","title":"PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search","date":null,"month_inferred_from_arxiv_id":"2023-04","title_source":"archive","repo":"alibaba/lightweight-neural-architecture-search","path":"modelscope/utils/logger.py","file_url":"https://github.com/alibaba/lightweight-neural-architecture-search/blob/HEAD/modelscope/utils/logger.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":"21b93c8efc0f5b5c","mcp_get_code":{"code_sha256":"21b93c8efc0f5b5c"}},{"arxiv_id":"2304.13098","paper":"/paper/uncovering-the-representation-of-spiking","title":"Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"intelligent-computing-lab-yale/snncka","path":"functions.py","file_url":"https://github.com/intelligent-computing-lab-yale/snncka/blob/HEAD/functions.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2304.11062","paper":"/paper/scaling-transformer-to-1m-tokens-and-beyond","title":"Scaling Transformer to 1M tokens and beyond with RMT","date":"2023-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"booydar/transformer-xl","path":"pytorch/utils/exp_utils.py","file_url":"https://github.com/booydar/transformer-xl/blob/HEAD/pytorch/utils/exp_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":"d142e8a68b535885","mcp_get_code":{"code_sha256":"d142e8a68b535885"}},{"arxiv_id":"2304.08424","paper":"/paper/long-term-forecasting-with-tide-time-series","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","date":"2023-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unit8co/darts","path":"darts/logging.py","file_url":"https://github.com/unit8co/darts/blob/HEAD/darts/logging.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":"85c45ee1e91339b5","mcp_get_code":{"code_sha256":"85c45ee1e91339b5"}},{"arxiv_id":"2304.05758","paper":"/paper/best-practices-for-2-body-pose-forecasting","title":"Best Practices for 2-Body Pose Forecasting","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edodema/BestPractices2Body","path":"src/utils/logger.py","file_url":"https://github.com/edodema/BestPractices2Body/blob/HEAD/src/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7660a1c90472fa55","mcp_get_code":{"code_sha256":"7660a1c90472fa55"}},{"arxiv_id":"2304.02198","paper":"/paper/eigenfold-generative-protein-structure","title":"EigenFold: Generative Protein Structure Prediction with Diffusion Models","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bjing2016/EigenFold","path":"utils/logging.py","file_url":"https://github.com/bjing2016/EigenFold/blob/HEAD/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0df91f35231085f8","mcp_get_code":{"code_sha256":"0df91f35231085f8"}},{"arxiv_id":"2303.12766","paper":"/paper/spherical-transformer-for-lidar-based-3d","title":"Spherical Transformer for LiDAR-based 3D Recognition","date":"2023-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvlab-research/sphereformer","path":"util/logger.py","file_url":"https://github.com/dvlab-research/sphereformer/blob/HEAD/util/logger.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":"e985f384ed063aef","mcp_get_code":{"code_sha256":"e985f384ed063aef"}},{"arxiv_id":"2303.11066","paper":"/paper/boosting-semi-supervised-learning-by","title":"Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/FullMatch","path":"utils.py","file_url":"https://github.com/megvii-research/FullMatch/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":"f134f238fbf660cc","mcp_get_code":{"code_sha256":"f134f238fbf660cc"}},{"arxiv_id":"2303.10909","paper":"/paper/graph-neural-rough-differential-equations-for","title":"Graph Neural Rough Differential Equations for Traffic Forecasting","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeongwhanchoi/STG-NCDE","path":"lib/logger.py","file_url":"https://github.com/jeongwhanchoi/STG-NCDE/blob/HEAD/lib/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996764623af1eab9","mcp_get_code":{"code_sha256":"996764623af1eab9"}},{"arxiv_id":"2303.06871","paper":"/paper/physics-driven-machine-learning-models","title":"Physics-driven machine learning models coupling PyTorch and Firedrake","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nbouziani/physics-driven-ml","path":"physics_driven_ml/utils.py","file_url":"https://github.com/nbouziani/physics-driven-ml/blob/HEAD/physics_driven_ml/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab9d264deab3e5f3","mcp_get_code":{"code_sha256":"ab9d264deab3e5f3"}},{"arxiv_id":"2303.00233","paper":"/paper/single-cell-multimodal-prediction-via","title":"Single-Cell Multimodal Prediction via Transformers","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omicsml/scmoformer","path":"utils/config.py","file_url":"https://github.com/omicsml/scmoformer/blob/HEAD/utils/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"301125ad54fbdd97","mcp_get_code":{"code_sha256":"301125ad54fbdd97"}},{"arxiv_id":"2302.09018","paper":"/paper/self-supervised-action-representation","title":"Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YujieOuO/PSTL","path":"logger.py","file_url":"https://github.com/YujieOuO/PSTL/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f97780826ffa265","mcp_get_code":{"code_sha256":"5f97780826ffa265"}},{"arxiv_id":"2301.12132","paper":"/paper/autopeft-automatic-configuration-search-for","title":"AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridgeltl/autopeft","path":"logger.py","file_url":"https://github.com/cambridgeltl/autopeft/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"2301.08227","paper":"/paper/diffusion-based-conditional-ecg-generation","title":"Diffusion-based Conditional ECG Generation with Structured State Space Models","date":"2023-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4healthuol/sssd-ecg","path":"src/sssd/models/S4Model.py","file_url":"https://github.com/ai4healthuol/sssd-ecg/blob/HEAD/src/sssd/models/S4Model.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2212.14427","paper":"/paper/efficient-movie-scene-detection-using-state","title":"Efficient Movie Scene Detection using State-Space Transformers","date":"2022-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"md-mohaiminul/trans4mer","path":"trans4mer/model/s4.py","file_url":"https://github.com/md-mohaiminul/trans4mer/blob/HEAD/trans4mer/model/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2212.09651","paper":"/paper/cross-lingual-retrieval-augmented-prompt-for","title":"Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages","date":"2022-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ercong21/parc","path":"log.py","file_url":"https://github.com/ercong21/parc/blob/HEAD/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dfdabda1fafd3d9e","mcp_get_code":{"code_sha256":"dfdabda1fafd3d9e"}},{"arxiv_id":"2212.08208","paper":"/paper/location-aware-adaptive-denormalization-a","title":"Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting","date":"2022-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hakamshams/loan","path":"utils/utils.py","file_url":"https://github.com/hakamshams/loan/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":"c8c578dc7150039f","mcp_get_code":{"code_sha256":"c8c578dc7150039f"}},{"arxiv_id":"2211.12914","paper":"/paper/open-vocabulary-attribute-detection","title":"Open-vocabulary Attribute Detection","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OVAD-Benchmark/ovad-bechmark-code","path":"ovamc/misc.py","file_url":"https://github.com/OVAD-Benchmark/ovad-bechmark-code/blob/HEAD/ovamc/misc.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":"811f379c87ce6257","mcp_get_code":{"code_sha256":"811f379c87ce6257"}},{"arxiv_id":"2211.11176","paper":"/paper/spatiotemporal-modeling-of-multivariate","title":"Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsy935/graphs4mer","path":"model/s4.py","file_url":"https://github.com/tsy935/graphs4mer/blob/HEAD/model/s4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2211.07349","paper":"/paper/finding-skill-neurons-in-pre-trained","title":"Finding Skill Neurons in Pre-trained Transformer-based Language Models","date":"2022-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-KEG/Skill-Neuron","path":"src/log.py","file_url":"https://github.com/THU-KEG/Skill-Neuron/blob/HEAD/src/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9d1721da5040dcc","mcp_get_code":{"code_sha256":"e9d1721da5040dcc"}},{"arxiv_id":"2211.06552","paper":"/paper/collecting-interactive-multi-modal-datasets","title":"Collecting Interactive Multi-modal Datasets for Grounded Language Understanding","date":"2022-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iglu-contest/iglu-dataset","path":"mturk_scripts/common/logger.py","file_url":"https://github.com/iglu-contest/iglu-dataset/blob/HEAD/mturk_scripts/common/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a40668326b78875","mcp_get_code":{"code_sha256":"7a40668326b78875"}},{"arxiv_id":"2210.10318","paper":"/paper/gaussian-bernoulli-rbms-without-tears","title":"Gaussian-Bernoulli RBMs Without Tears","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lrjconan/grbm","path":"utils.py","file_url":"https://github.com/lrjconan/grbm/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":"8569ed5e16bfff42","mcp_get_code":{"code_sha256":"8569ed5e16bfff42"}},{"arxiv_id":"2210.09337","paper":"/paper/robust-imitation-of-a-few-demonstrations-with","title":"Robust Imitation of a Few Demonstrations with a Backwards Model","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jypark0/bmil","path":"src/logger.py","file_url":"https://github.com/jypark0/bmil/blob/HEAD/src/logger.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":"edca5c1880cc6ad4","mcp_get_code":{"code_sha256":"edca5c1880cc6ad4"}},{"arxiv_id":"2210.01776","paper":"/paper/diffdock-diffusion-steps-twists-and-turns-for","title":"DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gcorso/diffdock","path":"utils/sampling.py","file_url":"https://github.com/gcorso/diffdock/blob/HEAD/utils/sampling.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":"8b2cbf9f7298d8ad","mcp_get_code":{"code_sha256":"8b2cbf9f7298d8ad"}},{"arxiv_id":"2209.10438","paper":"/paper/partial-information-decomposition-reveals-the","title":"A Measure of the Complexity of Neural Representations based on Partial Information Decomposition","date":"2022-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"priesemann-group/nninfo","path":"nninfo/logger.py","file_url":"https://github.com/priesemann-group/nninfo/blob/HEAD/nninfo/logger.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":"26ed40d9f57ad645","mcp_get_code":{"code_sha256":"26ed40d9f57ad645"}},{"arxiv_id":"2209.04766","paper":"/paper/towards-sparsification-of-graph-neural","title":"Towards Sparsification of Graph Neural Networks","date":"2022-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"harveyp123/iccd_sptrn_slr","path":"SLR_GCN_Node_Class/utils.py","file_url":"https://github.com/harveyp123/iccd_sptrn_slr/blob/HEAD/SLR_GCN_Node_Class/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"285a08a5c6aa47e3","mcp_get_code":{"code_sha256":"285a08a5c6aa47e3"}},{"arxiv_id":"2208.08195","paper":"/paper/learning-transductions-to-test-systematic","title":"Benchmarking Compositionality with Formal Languages","date":"2022-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valvoda/neuraltransducer","path":"src/util.py","file_url":"https://github.com/valvoda/neuraltransducer/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":"03bea59a9689bb8f","mcp_get_code":{"code_sha256":"03bea59a9689bb8f"}},{"arxiv_id":"2207.12201","paper":"/paper/calibrated-one-class-classification-for","title":"Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection","date":"2022-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuhongzuo/couta","path":"main_utils.py","file_url":"https://github.com/xuhongzuo/couta/blob/HEAD/main_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":"22c9707971cf2a58","mcp_get_code":{"code_sha256":"22c9707971cf2a58"}},{"arxiv_id":"2207.08548","paper":"/paper/gate-gated-additive-tree-ensemble-for-tabular","title":"GANDALF: Gated Adaptive Network for Deep Automated Learning of Features","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manujosephv/pytorch_tabular","path":"src/pytorch_tabular/utils/logger.py","file_url":"https://github.com/manujosephv/pytorch_tabular/blob/HEAD/src/pytorch_tabular/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"237648a939ccf6d0","mcp_get_code":{"code_sha256":"237648a939ccf6d0"}},{"arxiv_id":"2207.00012","paper":"/paper/reliable-representations-make-a-stronger","title":"Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN","date":"2022-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"likuanppd/stable","path":"utils.py","file_url":"https://github.com/likuanppd/stable/blob/HEAD/utils.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2206.07557","paper":"/paper/how-to-reduce-change-detection-to-semantic","title":"How to Reduce Change Detection to Semantic Segmentation","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DoctorKey/C-3PO","path":"src/log.py","file_url":"https://github.com/DoctorKey/C-3PO/blob/HEAD/src/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79c6f92043597ca3","mcp_get_code":{"code_sha256":"79c6f92043597ca3"}},{"arxiv_id":"2206.02280","paper":"/paper/annotation-error-detection-analyzing-the-past","title":"Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ukplab/nessie","path":"nessie/util.py","file_url":"https://github.com/ukplab/nessie/blob/HEAD/nessie/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"854b6a3d2d148947","mcp_get_code":{"code_sha256":"854b6a3d2d148947"}},{"arxiv_id":"2206.01204","paper":"/paper/siamese-image-modeling-for-self-supervised","title":"Siamese Image Modeling for Self-Supervised Vision Representation Learning","date":"2022-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fundamentalvision/unigrad","path":"project/utils.py","file_url":"https://github.com/fundamentalvision/unigrad/blob/HEAD/project/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":"3637b63ede9883b8","mcp_get_code":{"code_sha256":"3637b63ede9883b8"}},{"arxiv_id":"2205.12713","paper":"/paper/jtrans-jump-aware-transformer-for-binary-code","title":"jTrans: Jump-Aware Transformer for Binary Code Similarity","date":null,"month_inferred_from_arxiv_id":"2022-05","title_source":"archive","repo":"vul337/jtrans","path":"finetune.py","file_url":"https://github.com/vul337/jtrans/blob/HEAD/finetune.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":"2fd02470bd3c6935","mcp_get_code":{"code_sha256":"2fd02470bd3c6935"}},{"arxiv_id":"2205.12713","paper":"/paper/jtrans-jump-aware-transformer-for-binary-code","title":"jTrans: Jump-Aware Transformer for Binary Code Similarity","date":null,"month_inferred_from_arxiv_id":"2022-05","title_source":"archive","repo":"vul337/jtrans","path":"eval_save.py","file_url":"https://github.com/vul337/jtrans/blob/HEAD/eval_save.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":"e50f100ff38c365a","mcp_get_code":{"code_sha256":"e50f100ff38c365a"}},{"arxiv_id":"2205.08987","paper":"/paper/trading-positional-complexity-vs-deepness-in","title":"Trading Positional Complexity vs. Deepness in Coordinate Networks","date":"2022-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"osiriszjq/complex_encoding","path":"2D_random_points.py","file_url":"https://github.com/osiriszjq/complex_encoding/blob/HEAD/2D_random_points.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d8dad09165d593fd","mcp_get_code":{"code_sha256":"d8dad09165d593fd"}},{"arxiv_id":"2205.08897","paper":"/paper/film-frequency-improved-legendre-memory-model","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","date":"2022-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianzhou2011/FiLM","path":"layers/S4.py","file_url":"https://github.com/tianzhou2011/FiLM/blob/HEAD/layers/S4.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2204.09914","paper":"/paper/cpgnet-cascade-point-grid-fusion-network-for","title":"CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation","date":"2022-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huixiancheng/PCB-RandNet","path":"help_utils.py","file_url":"https://github.com/huixiancheng/PCB-RandNet/blob/HEAD/help_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7029f7b29b9d7c91","mcp_get_code":{"code_sha256":"7029f7b29b9d7c91"}},{"arxiv_id":"2204.09633","paper":"/paper/survlatent-ode-a-neural-ode-based-time-to","title":"SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction","date":"2022-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itmoon7/survlatent_ode","path":"lib/utils.py","file_url":"https://github.com/itmoon7/survlatent_ode/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":"6e6961df228cbc5a","mcp_get_code":{"code_sha256":"6e6961df228cbc5a"}},{"arxiv_id":"2204.01692","paper":"/paper/long-movie-clip-classification-with-state","title":"Long Movie Clip Classification with State-Space Video Models","date":"2022-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"md-mohaiminul/ViS4mer","path":"models.py","file_url":"https://github.com/md-mohaiminul/ViS4mer/blob/HEAD/models.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":"61139ec62260b411","mcp_get_code":{"code_sha256":"61139ec62260b411"}},{"arxiv_id":"2203.17271","paper":"/paper/do-vision-language-pretrained-models-learn","title":"Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tttyuntian/vlm_primitive_concepts","path":"vlm_concept/cub_200_2011/precompute_features.py","file_url":"https://github.com/tttyuntian/vlm_primitive_concepts/blob/HEAD/vlm_concept/cub_200_2011/precompute_features.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8c9945f5c6ad1124","mcp_get_code":{"code_sha256":"8c9945f5c6ad1124"}},{"arxiv_id":"2203.17271","paper":"/paper/do-vision-language-pretrained-models-learn","title":"Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tttyuntian/vlm_primitive_concepts","path":"vlm_concept/mit_states/train_retrieval_model.py","file_url":"https://github.com/tttyuntian/vlm_primitive_concepts/blob/HEAD/vlm_concept/mit_states/train_retrieval_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93bd857d933286f9","mcp_get_code":{"code_sha256":"93bd857d933286f9"}},{"arxiv_id":"2203.15296","paper":"/paper/frequency-dynamic-convolution-frequency","title":"Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frednam93/FDY-SED","path":"utils/settings.py","file_url":"https://github.com/frednam93/FDY-SED/blob/HEAD/utils/settings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f1a7881bda24d62","mcp_get_code":{"code_sha256":"5f1a7881bda24d62"}},{"arxiv_id":"2203.14508","paper":"/paper/stratified-transformer-for-3d-point-cloud","title":"Stratified Transformer for 3D Point Cloud Segmentation","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvlab-research/stratified-transformer","path":"util/logger.py","file_url":"https://github.com/dvlab-research/stratified-transformer/blob/HEAD/util/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e985f384ed063aef","mcp_get_code":{"code_sha256":"e985f384ed063aef"}},{"arxiv_id":"2203.14415","paper":"/paper/mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/mugs","path":"utils.py","file_url":"https://github.com/sail-sg/mugs/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":"67bc6a24e6bba540","mcp_get_code":{"code_sha256":"67bc6a24e6bba540"}},{"arxiv_id":"2203.14197","paper":"/paper/long-tailed-recognition-via-weight-balancing","title":"Long-Tailed Recognition via Weight Balancing","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hn410/exploring-weight-balancing-on-long-tailed-recognition-problem","path":"utils/regularizers.py","file_url":"https://github.com/hn410/exploring-weight-balancing-on-long-tailed-recognition-problem/blob/HEAD/utils/regularizers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9e06ce81e8a8384f","mcp_get_code":{"code_sha256":"9e06ce81e8a8384f"}},{"arxiv_id":"2203.13556","paper":"/paper/deformable-butterfly-a-highly-structured-and-1","title":"Deformable Butterfly: A Highly Structured and Sparse Linear Transform","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruilin0212/debut","path":"Fine_tuning/utils/common.py","file_url":"https://github.com/ruilin0212/debut/blob/HEAD/Fine_tuning/utils/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74079da8f8ca5a1c","mcp_get_code":{"code_sha256":"74079da8f8ca5a1c"}},{"arxiv_id":"2203.04287","paper":"/paper/a-simple-multi-modality-transfer-learning","title":"A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rzhao-zhsq/cv-slt","path":"modelling/translation.py","file_url":"https://github.com/rzhao-zhsq/cv-slt/blob/HEAD/modelling/translation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd6cc8d642098d59","mcp_get_code":{"code_sha256":"cd6cc8d642098d59"}},{"arxiv_id":"2203.03022","paper":"/paper/hear-2021-holistic-evaluation-of-audio","title":"HEAR: Holistic Evaluation of Audio Representations","date":"2022-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuralaudio/hear-eval-kit","path":"heareval/predictions/runner.py","file_url":"https://github.com/neuralaudio/hear-eval-kit/blob/HEAD/heareval/predictions/runner.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":"e85245b6910393ed","mcp_get_code":{"code_sha256":"e85245b6910393ed"}},{"arxiv_id":"2202.13514","paper":"/paper/strongsort-make-deepsort-great-again","title":"StrongSORT: Make DeepSORT Great Again","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goksenin-uav/strongsort-pip","path":"strongsort/log.py","file_url":"https://github.com/goksenin-uav/strongsort-pip/blob/HEAD/strongsort/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1fcd8693108efe91","mcp_get_code":{"code_sha256":"1fcd8693108efe91"}},{"arxiv_id":"2202.05656","paper":"/paper/interprettime-a-new-approach-for-the","title":"Evaluation of post-hoc interpretability methods in time-series classification","date":"2022-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hturbe/interprettime","path":"src/shared_utils/logger.py","file_url":"https://github.com/hturbe/interprettime/blob/HEAD/src/shared_utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80709348bb42438d","mcp_get_code":{"code_sha256":"80709348bb42438d"}},{"arxiv_id":"2201.05131","paper":"/paper/simreg-regression-as-a-simple-yet-effective","title":"SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation","date":"2022-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucdvision/simreg","path":"tools.py","file_url":"https://github.com/ucdvision/simreg/blob/HEAD/tools.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"2112.06318","paper":"/paper/contextualized-scene-imagination-for-1","title":"Contextualized Scene Imagination for Generative Commonsense Reasoning","date":"2021-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangpf3/imagine-and-verbalize","path":"imagination_learning/lib/model.py","file_url":"https://github.com/wangpf3/imagine-and-verbalize/blob/HEAD/imagination_learning/lib/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c958be3b74817df7","mcp_get_code":{"code_sha256":"c958be3b74817df7"}},{"arxiv_id":"2111.15664","paper":"/paper/donut-document-understanding-transformer","title":"OCR-free Document Understanding Transformer","date":"2021-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"impira/docquery","path":"src/docquery/config.py","file_url":"https://github.com/impira/docquery/blob/HEAD/src/docquery/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5df09cd2222c8bdc","mcp_get_code":{"code_sha256":"5df09cd2222c8bdc"}},{"arxiv_id":"2111.12273","paper":"/paper/sharpness-aware-quantization-for-deep-neural","title":"Sharpness-aware Quantization for Deep Neural Networks","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuang-group/saq","path":"core/logger.py","file_url":"https://github.com/zhuang-group/saq/blob/HEAD/core/logger.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":"5d93362f9df2f82e","mcp_get_code":{"code_sha256":"5d93362f9df2f82e"}},{"arxiv_id":"2111.09883","paper":"/paper/swin-transformer-v2-scaling-up-capacity-and","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2","path":"main_multi_gpu.py","file_url":"https://github.com/nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2/blob/HEAD/main_multi_gpu.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":"043c2f16bd404117","mcp_get_code":{"code_sha256":"043c2f16bd404117"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/mae","path":"src/logger.py","file_url":"https://github.com/yangyucheng000/mae/blob/HEAD/src/logger.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":"8765fe83bed1a28f","mcp_get_code":{"code_sha256":"8765fe83bed1a28f"}},{"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/logger.py","file_url":"https://github.com/persiaml/persia/blob/HEAD/persia/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a5cc4f297d394ff","mcp_get_code":{"code_sha256":"6a5cc4f297d394ff"}},{"arxiv_id":"2110.11945","paper":"/paper/soft-softmax-free-transformer-with-linear","title":"SOFT: Softmax-free Transformer with Linear Complexity","date":"2021-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/SOFT_MindSpore_Ascend","path":"src/logging.py","file_url":"https://github.com/fudan-zvg/SOFT_MindSpore_Ascend/blob/HEAD/src/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8765fe83bed1a28f","mcp_get_code":{"code_sha256":"8765fe83bed1a28f"}},{"arxiv_id":"2110.01256","paper":"/paper/revisiting-self-training-for-few-shot","title":"Revisiting Self-Training for Few-Shot Learning of Language Model","date":"2021-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timoschick/pet","path":"log.py","file_url":"https://github.com/timoschick/pet/blob/HEAD/log.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":"999a29a2aa943ed5","mcp_get_code":{"code_sha256":"999a29a2aa943ed5"}},{"arxiv_id":"2110.00527","paper":"/paper/consistent-explanations-by-contrastive","title":"Consistent Explanations by Contrastive Learning","date":"2021-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UCDvision/CGC","path":"train_eval_cgc.py","file_url":"https://github.com/UCDvision/CGC/blob/HEAD/train_eval_cgc.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"2109.13880","paper":"/paper/single-dataset-experts-for-multi-dataset","title":"Single-dataset Experts for Multi-dataset Question Answering","date":"2021-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/MADE","path":"src/utils/logging.py","file_url":"https://github.com/princeton-nlp/MADE/blob/HEAD/src/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13444437d74ae65b","mcp_get_code":{"code_sha256":"13444437d74ae65b"}},{"arxiv_id":"2108.09105","paper":"/paper/airbert-in-domain-pretraining-for-vision-and","title":"Airbert: In-domain Pretraining for Vision-and-Language Navigation","date":"2021-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airbert-vln/airbert","path":"utils/misc.py","file_url":"https://github.com/airbert-vln/airbert/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":"34279e5b4ed2577f","mcp_get_code":{"code_sha256":"34279e5b4ed2577f"}},{"arxiv_id":"2108.02927","paper":"/paper/dolg-single-stage-image-retrieval-with-deep","title":"DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features","date":"2021-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feymanpriv/DOLG","path":"core/logging.py","file_url":"https://github.com/feymanpriv/DOLG/blob/HEAD/core/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e4e129cb77e7f8b","mcp_get_code":{"code_sha256":"8e4e129cb77e7f8b"}},{"arxiv_id":"2107.08430","paper":"/paper/yolox-exceeding-yolo-series-in-2021","title":"YOLOX: Exceeding YOLO Series in 2021","date":"2021-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2023-MindSpore-1/ms-code-31","path":"src/logger.py","file_url":"https://github.com/2023-MindSpore-1/ms-code-31/blob/HEAD/src/logger.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":"bb879be65ea0ad0d","mcp_get_code":{"code_sha256":"bb879be65ea0ad0d"}},{"arxiv_id":"2106.13898","paper":"/paper/closed-form-continuous-depth-models","title":"Closed-form Continuous-time Neural Models","date":"2021-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raminmh/CfC","path":"duv_utils.py","file_url":"https://github.com/raminmh/CfC/blob/HEAD/duv_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":"6e6961df228cbc5a","mcp_get_code":{"code_sha256":"6e6961df228cbc5a"}},{"arxiv_id":"2106.06716","paper":"/paper/ds-transunet-dual-swin-transformer-u-net-for","title":"DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation","date":"2021-06-12","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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"2106.06295","paper":"/paper/going-beyond-linear-transformers-with","title":"Going Beyond Linear Transformers with Recurrent Fast Weight Programmers","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDSIA/lmtool-fwms","path":"src/utils/exp_utils.py","file_url":"https://github.com/IDSIA/lmtool-fwms/blob/HEAD/src/utils/exp_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":"d142e8a68b535885","mcp_get_code":{"code_sha256":"d142e8a68b535885"}},{"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":"src/logger.py","file_url":"https://github.com/smkim7-kr/AdaMatch-pytorch/blob/HEAD/src/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6066adc7a58afde6","mcp_get_code":{"code_sha256":"6066adc7a58afde6"}},{"arxiv_id":"2106.02885","paper":"/paper/category-contrast-for-unsupervised-domain","title":"Category Contrast for Unsupervised Domain Adaptation in Visual Tasks","date":"2021-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxhuang0508/CaCo","path":"caco_proda_finetune/calc_prototype.py","file_url":"https://github.com/jxhuang0508/CaCo/blob/HEAD/caco_proda_finetune/calc_prototype.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac7a106d5297c790","mcp_get_code":{"code_sha256":"ac7a106d5297c790"}},{"arxiv_id":"2106.01357","paper":"/paper/diffusion-schrodinger-bridge-with","title":"Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling","date":"2021-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maxencenoble/tree-diffusion-schrodinger-bridge","path":"bridge/runners/ipf.py","file_url":"https://github.com/maxencenoble/tree-diffusion-schrodinger-bridge/blob/HEAD/bridge/runners/ipf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30064cd4e8da4011","mcp_get_code":{"code_sha256":"30064cd4e8da4011"}},{"arxiv_id":"2106.01357","paper":"/paper/diffusion-schrodinger-bridge-with","title":"Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling","date":"2021-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JTT94/diffusion_schrodinger_bridge","path":"bridge/runners/ipf.py","file_url":"https://github.com/JTT94/diffusion_schrodinger_bridge/blob/HEAD/bridge/runners/ipf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b15b7368db6c2575","mcp_get_code":{"code_sha256":"b15b7368db6c2575"}},{"arxiv_id":"2105.07085","paper":"/paper/mutualnet-adaptive-convnet-via-mutual","title":"MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations","date":"2021-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taoyang1122/MutualNet","path":"utils/setlogger.py","file_url":"https://github.com/taoyang1122/MutualNet/blob/HEAD/utils/setlogger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84a10f5778846b16","mcp_get_code":{"code_sha256":"84a10f5778846b16"}},{"arxiv_id":"2105.04051","paper":"/paper/aggregating-from-multiple-target-shifted","title":"Aggregating From Multiple Target-Shifted Sources","date":"2021-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjshui/wadn","path":"utils.py","file_url":"https://github.com/cjshui/wadn/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":"e4b0642506057969","mcp_get_code":{"code_sha256":"e4b0642506057969"}},{"arxiv_id":"2105.00956","paper":"/paper/unignn-a-unified-framework-for-graph-and","title":"UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks","date":"2021-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OneForward/UniGNN","path":"logger.py","file_url":"https://github.com/OneForward/UniGNN/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22a0485e20b0b4a0","mcp_get_code":{"code_sha256":"22a0485e20b0b4a0"}},{"arxiv_id":"2104.12233","paper":"/paper/the-5th-ai-city-challenge","title":"The 5th AI City Challenge","date":"2021-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fredfung007/cityflow-nl","path":"baseline/utils.py","file_url":"https://github.com/fredfung007/cityflow-nl/blob/HEAD/baseline/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":"a4fba287a546af42","mcp_get_code":{"code_sha256":"a4fba287a546af42"}},{"arxiv_id":"2101.02477","paper":"/paper/gan-control-explicitly-controllable-gans","title":"GAN-Control: Explicitly Controllable GANs","date":"2021-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/gan-control","path":"src/gan_control/models/gan_model.py","file_url":"https://github.com/amazon-research/gan-control/blob/HEAD/src/gan_control/models/gan_model.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":"86dcd3deaf9ad8a8","mcp_get_code":{"code_sha256":"86dcd3deaf9ad8a8"}},{"arxiv_id":"2010.13009","paper":"/paper/discriminative-nearest-neighbor-few-shot","title":"Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference","date":"2020-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/DNNC-few-shot-intent","path":"models/utils.py","file_url":"https://github.com/salesforce/DNNC-few-shot-intent/blob/HEAD/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":"48db82b94f4da452","mcp_get_code":{"code_sha256":"48db82b94f4da452"}},{"arxiv_id":"2010.02534","paper":"/paper/an-empirical-study-of-tokenization-strategies","title":"An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/kortok","path":"tasks/logger.py","file_url":"https://github.com/kakaobrain/kortok/blob/HEAD/tasks/logger.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":"8b586b3fbd2c898f","mcp_get_code":{"code_sha256":"8b586b3fbd2c898f"}},{"arxiv_id":"2007.12107","paper":"/paper/few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YoungXIAO13/PoseContrast","path":"src/utils/logger.py","file_url":"https://github.com/YoungXIAO13/PoseContrast/blob/HEAD/src/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"57069fdc71bccf4c","mcp_get_code":{"code_sha256":"57069fdc71bccf4c"}},{"arxiv_id":"2006.11267","paper":"/paper/fast-matrix-square-roots-with-applications-to","title":"Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gpleiss/ciq_experiments","path":"svgp/logger.py","file_url":"https://github.com/gpleiss/ciq_experiments/blob/HEAD/svgp/logger.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":"fb5b4b75bd55fd1c","mcp_get_code":{"code_sha256":"fb5b4b75bd55fd1c"}},{"arxiv_id":"2006.10175","paper":"/paper/flows-succeed-where-gans-fail-lessons-from","title":"An Empirical Comparison of GANs and Normalizing Flows for Density Estimation","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lliutianc/gan-flow","path":"util.py","file_url":"https://github.com/lliutianc/gan-flow/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":"0ef812b5a4f36435","mcp_get_code":{"code_sha256":"0ef812b5a4f36435"}},{"arxiv_id":"2006.09286","paper":"/paper/on-the-computational-power-of-transformers","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satwik77/Transformer-Computation-Analysis","path":"Transformer/src/utils/logger.py","file_url":"https://github.com/satwik77/Transformer-Computation-Analysis/blob/HEAD/Transformer/src/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ebe3d1e68daeb780","mcp_get_code":{"code_sha256":"ebe3d1e68daeb780"}},{"arxiv_id":"2006.09102","paper":"/paper/ucsg-net-unsupervised-discovering-of","title":"UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kacperkan/ucsgnet","path":"ucsgnet/common.py","file_url":"https://github.com/kacperkan/ucsgnet/blob/HEAD/ucsgnet/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b46b02c3562dfe3","mcp_get_code":{"code_sha256":"3b46b02c3562dfe3"}},{"arxiv_id":"2006.07989","paper":"/paper/gradaug-a-new-regularization-method-for-deep","title":"GradAug: A New Regularization Method for Deep Neural Networks","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taoyang1122/GradAug","path":"utils/setlogger.py","file_url":"https://github.com/taoyang1122/GradAug/blob/HEAD/utils/setlogger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84a10f5778846b16","mcp_get_code":{"code_sha256":"84a10f5778846b16"}},{"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":"a65d0752b6dd6c36","mcp_get_code":{"code_sha256":"a65d0752b6dd6c36"}},{"arxiv_id":"2005.01463","paper":"/paper/meshfreeflownet-a-physics-constrained-deep","title":"MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework","date":"2020-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maxjiang93/space_time_pde","path":"src/train_utils.py","file_url":"https://github.com/maxjiang93/space_time_pde/blob/HEAD/src/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ebfbc7758b348cfb","mcp_get_code":{"code_sha256":"ebfbc7758b348cfb"}},{"arxiv_id":"2004.12585","paper":"/paper/a-batch-normalized-inference-network-keeps","title":"A Batch Normalized Inference Network Keeps the KL Vanishing Away","date":"2020-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valdersoul/bn-vae","path":"exp_utils.py","file_url":"https://github.com/valdersoul/bn-vae/blob/HEAD/exp_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a751ef26f71f337","mcp_get_code":{"code_sha256":"2a751ef26f71f337"}},{"arxiv_id":"2004.11362","paper":"/paper/supervised-contrastive-learning","title":"Supervised Contrastive Learning","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaperCodeReview/SupCL-TF","path":"common.py","file_url":"https://github.com/PaperCodeReview/SupCL-TF/blob/HEAD/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9cdcc13fe49820c","mcp_get_code":{"code_sha256":"e9cdcc13fe49820c"}},{"arxiv_id":"2004.10964","paper":"/paper/don-t-stop-pretraining-adapt-language-models","title":"Don't Stop Pretraining: Adapt Language Models to Domains and Tasks","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shizhediao/t-dna","path":"TDNA/util.py","file_url":"https://github.com/shizhediao/t-dna/blob/HEAD/TDNA/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":"925a37d315d717fb","mcp_get_code":{"code_sha256":"925a37d315d717fb"}},{"arxiv_id":"2004.10934","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JisuHann/MEME-Tracking","path":"deep_sort_pytorch/utils/log.py","file_url":"https://github.com/JisuHann/MEME-Tracking/blob/HEAD/deep_sort_pytorch/utils/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1fcd8693108efe91","mcp_get_code":{"code_sha256":"1fcd8693108efe91"}},{"arxiv_id":"2004.10934","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanghuiyao/yolov5_mindspore","path":"src/logger.py","file_url":"https://github.com/zhanghuiyao/yolov5_mindspore/blob/HEAD/src/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8c911ff2d998e4c9","mcp_get_code":{"code_sha256":"8c911ff2d998e4c9"}},{"arxiv_id":"2004.08728","paper":"/paper/simalign-high-quality-word-alignments-without","title":"SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings","date":"2020-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cisnlp/simalign","path":"simalign/simalign.py","file_url":"https://github.com/cisnlp/simalign/blob/HEAD/simalign/simalign.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":"9be91b30408c62ce","mcp_get_code":{"code_sha256":"9be91b30408c62ce"}},{"arxiv_id":"2003.13678","paper":"/paper/designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaperCodeReview/RegNet-TF","path":"common.py","file_url":"https://github.com/PaperCodeReview/RegNet-TF/blob/HEAD/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9cdcc13fe49820c","mcp_get_code":{"code_sha256":"e9cdcc13fe49820c"}},{"arxiv_id":"2003.04297","paper":"/paper/improved-baselines-with-momentum-contrastive","title":"Improved Baselines with Momentum Contrastive Learning","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaperCodeReview/MoCo-TF","path":"common.py","file_url":"https://github.com/PaperCodeReview/MoCo-TF/blob/HEAD/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9cdcc13fe49820c","mcp_get_code":{"code_sha256":"e9cdcc13fe49820c"}},{"arxiv_id":"2002.11661","paper":"/paper/compact-representation-of-uncertainty-in-1","title":"Data Structures & Algorithms for Exact Inference in Hierarchical Clustering","date":"2020-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SebastianMacaluso/ClusterTrellis","path":"src/ClusterTrellis/utils.py","file_url":"https://github.com/SebastianMacaluso/ClusterTrellis/blob/HEAD/src/ClusterTrellis/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a24f023d0dcd220","mcp_get_code":{"code_sha256":"4a24f023d0dcd220"}},{"arxiv_id":"2002.02798","paper":"/paper/how-to-train-your-neural-ode","title":"How to train your neural ODE: the world of Jacobian and kinetic regularization","date":"2020-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"D-hash-code/ffjord-rnode-finalweek-mnist","path":"lib/utils.py","file_url":"https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/blob/HEAD/lib/utils.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"1911.07532","paper":"/paper/graph-neural-ordinary-differential-equations","title":"Graph Neural Ordinary Differential Equations","date":"2019-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caidao22/pnode","path":"examples-pnode/train-Cifar10.py","file_url":"https://github.com/caidao22/pnode/blob/HEAD/examples-pnode/train-Cifar10.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"1911.03809","paper":"/paper/meta-label-correction-for-learning-with-weak-1","title":"Meta Label Correction for Noisy Label Learning","date":"2019-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/mlc","path":"logger.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22905be7a3378c0f","mcp_get_code":{"code_sha256":"22905be7a3378c0f"}},{"arxiv_id":"1911.03082","paper":"/paper/composition-based-multi-relational-graph","title":"Composition-based Multi-Relational Graph Convolutional Networks","date":"2019-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"malllabiisc/CompGCN","path":"helper.py","file_url":"https://github.com/malllabiisc/CompGCN/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":"4ecef8549fab0398","mcp_get_code":{"code_sha256":"4ecef8549fab0398"}},{"arxiv_id":"1911.00219","paper":"/paper/interacte-improving-convolution-based","title":"InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions","date":"2019-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"malllabiisc/InteractE","path":"helper.py","file_url":"https://github.com/malllabiisc/InteractE/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":"c298c38f28c4410d","mcp_get_code":{"code_sha256":"c298c38f28c4410d"}},{"arxiv_id":"1910.05895","paper":"/paper/transformers-without-tears-improving-the","title":"Transformers without Tears: Improving the Normalization of Self-Attention","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tnq177/transformers_without_tears","path":"utils.py","file_url":"https://github.com/tnq177/transformers_without_tears/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":"6375f131987193cf","mcp_get_code":{"code_sha256":"6375f131987193cf"}},{"arxiv_id":"1910.00760","paper":"/paper/efficient-graph-generation-with-graph","title":"Efficient Graph Generation with Graph Recurrent Attention Networks","date":"2019-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lrjconan/GRAN","path":"utils/logger.py","file_url":"https://github.com/lrjconan/GRAN/blob/HEAD/utils/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8569ed5e16bfff42","mcp_get_code":{"code_sha256":"8569ed5e16bfff42"}},{"arxiv_id":"1909.12605","paper":"/paper/towards-real-time-multi-object-tracking","title":"Towards Real-Time Multi-Object Tracking","date":"2019-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LKLQQ/JDE","path":"src/log.py","file_url":"https://github.com/LKLQQ/JDE/blob/HEAD/src/log.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":"3d8013e60355dac6","mcp_get_code":{"code_sha256":"3d8013e60355dac6"}},{"arxiv_id":"1908.03195","paper":"/paper/lvis-a-dataset-for-large-vocabulary-instance-1","title":"LVIS: A Dataset for Large Vocabulary Instance Segmentation","date":"2019-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"craston/object_detection_cib","path":"kod/lightning/logger.py","file_url":"https://github.com/craston/object_detection_cib/blob/HEAD/kod/lightning/logger.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":"d13d820fb1f13532","mcp_get_code":{"code_sha256":"d13d820fb1f13532"}},{"arxiv_id":"1907.10902","paper":"/paper/optuna-a-next-generation-hyperparameter","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","date":"2019-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"optuna/optuna","path":"optuna/logging.py","file_url":"https://github.com/optuna/optuna/blob/HEAD/optuna/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"532d732793640190","mcp_get_code":{"code_sha256":"532d732793640190"}},{"arxiv_id":"1907.10902","paper":"/paper/optuna-a-next-generation-hyperparameter","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","date":"2019-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crcrpar/benchmark-runner-ci","path":"optuna/logging.py","file_url":"https://github.com/crcrpar/benchmark-runner-ci/blob/HEAD/optuna/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81f35f190d0b3aff","mcp_get_code":{"code_sha256":"81f35f190d0b3aff"}},{"arxiv_id":"1907.10902","paper":"/paper/optuna-a-next-generation-hyperparameter","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","date":"2019-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rickyHong/optuna-repl","path":"optuna/logging.py","file_url":"https://github.com/rickyHong/optuna-repl/blob/HEAD/optuna/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16d3274a2492ce12","mcp_get_code":{"code_sha256":"16d3274a2492ce12"}},{"arxiv_id":"1907.10902","paper":"/paper/optuna-a-next-generation-hyperparameter","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","date":"2019-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yqian4/optuna","path":"optuna/logging.py","file_url":"https://github.com/yqian4/optuna/blob/HEAD/optuna/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2949bcda1225868b","mcp_get_code":{"code_sha256":"2949bcda1225868b"}},{"arxiv_id":"1906.05909","paper":"/paper/stand-alone-self-attention-in-vision-models","title":"Stand-Alone Self-Attention in Vision Models","date":"2019-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoeRoussy/adaptive-attention-in-cv","path":"config.py","file_url":"https://github.com/JoeRoussy/adaptive-attention-in-cv/blob/HEAD/config.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":"62d701bd9f6f7ffd","mcp_get_code":{"code_sha256":"62d701bd9f6f7ffd"}},{"arxiv_id":"1906.02735","paper":"/paper/residual-flows-for-invertible-generative","title":"Residual Flows for Invertible Generative Modeling","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eyalbetzalel/residual-flows","path":"lib/utils.py","file_url":"https://github.com/eyalbetzalel/residual-flows/blob/HEAD/lib/utils.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"1905.13214","paper":"/paper/on-network-design-spaces-for-visual","title":"On Network Design Spaces for Visual Recognition","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feymanpriv/pymetric","path":"metric/core/logging.py","file_url":"https://github.com/feymanpriv/pymetric/blob/HEAD/metric/core/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"8e4e129cb77e7f8b","mcp_get_code":{"code_sha256":"8e4e129cb77e7f8b"}},{"arxiv_id":"1905.00397","paper":"/paper/fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/fast-autoaugment","path":"FastAutoAugment/common.py","file_url":"https://github.com/kakaobrain/fast-autoaugment/blob/HEAD/FastAutoAugment/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6066adc7a58afde6","mcp_get_code":{"code_sha256":"6066adc7a58afde6"}},{"arxiv_id":"1905.00397","paper":"/paper/fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songyadong106/111","path":"skeleton/projects/others.py","file_url":"https://github.com/songyadong106/111/blob/HEAD/skeleton/projects/others.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":"58895526cd27ed71","mcp_get_code":{"code_sha256":"58895526cd27ed71"}},{"arxiv_id":"1904.12848","paper":"/paper/unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ildoonet/unsupervised-data-augmentation","path":"common.py","file_url":"https://github.com/ildoonet/unsupervised-data-augmentation/blob/HEAD/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":"6066adc7a58afde6","mcp_get_code":{"code_sha256":"6066adc7a58afde6"}},{"arxiv_id":"1904.08779","paper":"/paper/specaugment-a-simple-data-augmentation-method","title":"SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cosmoquester/speech-recognition","path":"speech_recognition/utils.py","file_url":"https://github.com/cosmoquester/speech-recognition/blob/HEAD/speech_recognition/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8faeedf5a5c01a","mcp_get_code":{"code_sha256":"0c8faeedf5a5c01a"}},{"arxiv_id":"1904.01617","paper":"/paper/attentive-mimicking-better-word-embeddings-by","title":"Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timoschick/form-context-model","path":"fcm/my_log.py","file_url":"https://github.com/timoschick/form-context-model/blob/HEAD/fcm/my_log.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":"9e8effe8d9693e74","mcp_get_code":{"code_sha256":"9e8effe8d9693e74"}},{"arxiv_id":"1903.05285","paper":"/paper/all-you-need-is-a-few-shifts-designing","title":"All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification","date":"2019-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/SparseShiftLayer","path":"utils.py","file_url":"https://github.com/hikvision-research/SparseShiftLayer/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":"014d87639fcaa13f","mcp_get_code":{"code_sha256":"014d87639fcaa13f"}},{"arxiv_id":"1903.03894","paper":"/paper/gnn-explainer-a-tool-for-post-hoc-explanation","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","date":"2019-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsy935/readmit-stgnn","path":"utils.py","file_url":"https://github.com/tsy935/readmit-stgnn/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":"725051c1c5a70fdf","mcp_get_code":{"code_sha256":"725051c1c5a70fdf"}},{"arxiv_id":"1903.03096","paper":"/paper/meta-dataset-a-dataset-of-datasets-for","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","date":"2019-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ebadrian/metadl","path":"metadl/core/parent_scoring/parent_scoring.py","file_url":"https://github.com/ebadrian/metadl/blob/HEAD/metadl/core/parent_scoring/parent_scoring.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":"e8e595468bf6e7e6","mcp_get_code":{"code_sha256":"e8e595468bf6e7e6"}},{"arxiv_id":"1902.10903","paper":"/paper/bi-directional-cascade-network-for-perceptual","title":"Bi-Directional Cascade Network for Perceptual Edge Detection","date":"2019-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pkuCactus/BDCN","path":"log.py","file_url":"https://github.com/pkuCactus/BDCN/blob/HEAD/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8fa63d5b31a5e99f","mcp_get_code":{"code_sha256":"8fa63d5b31a5e99f"}},{"arxiv_id":"1902.09393","paper":"/paper/cooperative-learning-of-disjoint-syntax-and","title":"Cooperative Learning of Disjoint Syntax and Semantics","date":"2019-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/latent-treelstm","path":"utils.py","file_url":"https://github.com/facebookresearch/latent-treelstm/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"65f16c8097639496","mcp_get_code":{"code_sha256":"65f16c8097639496"}},{"arxiv_id":"1901.08255","paper":"/paper/confidence-based-graph-convolutional-networks","title":"Confidence-based Graph Convolutional Networks for Semi-Supervised Learning","date":"2019-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"malllabiisc/ConfGCN","path":"helper.py","file_url":"https://github.com/malllabiisc/ConfGCN/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":"abcf08bcebcb4fd8","mcp_get_code":{"code_sha256":"abcf08bcebcb4fd8"}},{"arxiv_id":"1810.05739","paper":"/paper/meansum-a-neural-model-for-unsupervised-multi","title":"MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization","date":"2018-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megagonlabs/coop","path":"coop/util.py","file_url":"https://github.com/megagonlabs/coop/blob/HEAD/coop/util.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":"3b11203099e292ae","mcp_get_code":{"code_sha256":"3b11203099e292ae"}},{"arxiv_id":"1809.07454","paper":"/paper/tasnet-surpassing-ideal-time-frequency","title":"Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation","date":"2018-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"funcwj/conv-tasnet","path":"nnet/libs/utils.py","file_url":"https://github.com/funcwj/conv-tasnet/blob/HEAD/nnet/libs/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d0a0123e9bc8a4c4","mcp_get_code":{"code_sha256":"d0a0123e9bc8a4c4"}},{"arxiv_id":"1809.03633","paper":"/paper/unsupervised-cross-lingual-transfer-of-word","title":"Unsupervised Cross-lingual Transfer of Word Embedding Spaces","date":"2018-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xrc10/unsup-cross-lingual-embedding-transfer","path":"src/runner.py","file_url":"https://github.com/xrc10/unsup-cross-lingual-embedding-transfer/blob/HEAD/src/runner.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":"a00df19c529a2278","mcp_get_code":{"code_sha256":"a00df19c529a2278"}},{"arxiv_id":"1809.02627","paper":"/paper/unity-a-general-platform-for-intelligent","title":"Unity: A General Platform for Intelligent Agents","date":"2018-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingxiaowei/MLAgents","path":"ml-agents-envs/mlagents_envs/logging_util.py","file_url":"https://github.com/dingxiaowei/MLAgents/blob/HEAD/ml-agents-envs/mlagents_envs/logging_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":"0d1db9baf7692a97","mcp_get_code":{"code_sha256":"0d1db9baf7692a97"}},{"arxiv_id":"1807.11164","paper":"/paper/shufflenet-v2-practical-guidelines-for","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","date":"2018-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mnicnc404/CartoonGan-tensorflow","path":"logger.py","file_url":"https://github.com/mnicnc404/CartoonGan-tensorflow/blob/HEAD/logger.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":"f68f4a5d6df2ac7f","mcp_get_code":{"code_sha256":"f68f4a5d6df2ac7f"}},{"arxiv_id":"1806.09055","paper":"/paper/darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grtzsohalf/pt.darts","path":"utils.py","file_url":"https://github.com/grtzsohalf/pt.darts/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":"93204f7c934e37f7","mcp_get_code":{"code_sha256":"93204f7c934e37f7"}},{"arxiv_id":"1805.09501","paper":"/paper/autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abcp4/DAPytorch","path":"utils.py","file_url":"https://github.com/abcp4/DAPytorch/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":"93204f7c934e37f7","mcp_get_code":{"code_sha256":"93204f7c934e37f7"}},{"arxiv_id":"1804.02767","paper":"/paper/yolov3-an-incremental-improvement","title":"YOLOv3: An Incremental Improvement","date":"2018-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanghuiyao/yolov3_mindspore","path":"src/logger.py","file_url":"https://github.com/zhanghuiyao/yolov3_mindspore/blob/HEAD/src/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f938002f1fb9665f","mcp_get_code":{"code_sha256":"f938002f1fb9665f"}},{"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/utils/logger_utils.py","file_url":"https://github.com/owj0421/DeepFashion/blob/HEAD/src/utils/logger_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":"b5a7546483113625","mcp_get_code":{"code_sha256":"b5a7546483113625"}},{"arxiv_id":"1710.01329","paper":"/paper/improving-lexical-choice-in-neural-machine","title":"Improving Lexical Choice in Neural Machine Translation","date":"2017-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tnq177/improving_lexical_choice_in_nmt","path":"nmt/utils.py","file_url":"https://github.com/tnq177/improving_lexical_choice_in_nmt/blob/HEAD/nmt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"039e3fb281ed1d58","mcp_get_code":{"code_sha256":"039e3fb281ed1d58"}},{"arxiv_id":"1703.09179","paper":"/paper/transfer-learning-for-music-classification","title":"Transfer learning for music classification and regression tasks","date":"2017-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keunwoochoi/transfer_learning_music","path":"2_main_knn_svm_transfer.py","file_url":"https://github.com/keunwoochoi/transfer_learning_music/blob/HEAD/2_main_knn_svm_transfer.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":"6c40c4e33868790a","mcp_get_code":{"code_sha256":"6c40c4e33868790a"}},{"arxiv_id":"1703.05192","paper":"/paper/learning-to-discover-cross-domain-relations","title":"Learning to Discover Cross-Domain Relations with Generative Adversarial Networks","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"S-HuaBomb/DiscoGAN-Paddle","path":"discogan/image_translation.py","file_url":"https://github.com/S-HuaBomb/DiscoGAN-Paddle/blob/HEAD/discogan/image_translation.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":"29c35b8fd91ab7df","mcp_get_code":{"code_sha256":"29c35b8fd91ab7df"}},{"arxiv_id":"1702.05911","paper":"/paper/efficient-large-scale-approximate-nearest","title":"Efficient Large-scale Approximate Nearest Neighbor Search on the GPU","date":"2017-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"js1010/cuhnsw","path":"cuhnsw/aux.py","file_url":"https://github.com/js1010/cuhnsw/blob/HEAD/cuhnsw/aux.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":"dd9374387d715b25","mcp_get_code":{"code_sha256":"dd9374387d715b25"}},{"arxiv_id":"1702.01105","paper":"/paper/joint-2d-3d-semantic-data-for-indoor-scene","title":"Joint 2D-3D-Semantic Data for Indoor Scene Understanding","date":"2017-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamycheung/360BEV","path":"model/utils.py","file_url":"https://github.com/jamycheung/360BEV/blob/HEAD/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":"02d6e3bce0c2ff87","mcp_get_code":{"code_sha256":"02d6e3bce0c2ff87"}},{"arxiv_id":"1702.00758","paper":"/paper/hashnet-deep-learning-to-hash-by-continuation","title":"HashNet: Deep Learning to Hash by Continuation","date":"2017-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hatimwen/paddle_hashnet","path":"main_multi_gpu.py","file_url":"https://github.com/hatimwen/paddle_hashnet/blob/HEAD/main_multi_gpu.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":"3d300924bfb82d8f","mcp_get_code":{"code_sha256":"3d300924bfb82d8f"}},{"arxiv_id":"1603.08155","paper":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","date":"2016-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MartinBuessemeyer/Artistic-Texture-Control","path":"parameter_prediction_network/model_helper.py","file_url":"https://github.com/MartinBuessemeyer/Artistic-Texture-Control/blob/HEAD/parameter_prediction_network/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":"e2b8c34776dbc440","mcp_get_code":{"code_sha256":"e2b8c34776dbc440"}},{"arxiv_id":"1603.01360","paper":"/paper/neural-architectures-for-named-entity","title":"Neural Architectures for Named Entity Recognition","date":"2016-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guillaumegenthial/sequence_tagging","path":"model/general_utils.py","file_url":"https://github.com/guillaumegenthial/sequence_tagging/blob/HEAD/model/general_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":"75ed1ef1f8c3d22b","mcp_get_code":{"code_sha256":"75ed1ef1f8c3d22b"}},{"arxiv_id":"1603.01354","paper":"/paper/end-to-end-sequence-labeling-via-bi","title":"End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF","date":"2016-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SenticNet/aspect-extraction","path":"model/general_utils.py","file_url":"https://github.com/SenticNet/aspect-extraction/blob/HEAD/model/general_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":"75ed1ef1f8c3d22b","mcp_get_code":{"code_sha256":"75ed1ef1f8c3d22b"}},{"arxiv_id":"1512.02325","paper":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PuchatekwSzortach/voc_ssd","path":"net/utilities.py","file_url":"https://github.com/PuchatekwSzortach/voc_ssd/blob/HEAD/net/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53d58592c2d8cdc7","mcp_get_code":{"code_sha256":"53d58592c2d8cdc7"}},{"arxiv_id":"1509.09308","paper":"/paper/fast-algorithms-for-convolutional-neural","title":"Fast Algorithms for Convolutional Neural Networks","date":"2015-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adam-dziedzic/winograd","path":"log_utils.py","file_url":"https://github.com/adam-dziedzic/winograd/blob/HEAD/log_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":"00e7bde27a057b28","mcp_get_code":{"code_sha256":"00e7bde27a057b28"}},{"arxiv_id":"1509.02971","paper":"/paper/continuous-control-with-deep-reinforcement","title":"Continuous control with deep reinforcement learning","date":"2015-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bmeyers/VirtualMicrogridSegmentation","path":"virtual_microgrids/algorithms/ddpg.py","file_url":"https://github.com/bmeyers/VirtualMicrogridSegmentation/blob/HEAD/virtual_microgrids/algorithms/ddpg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"0132cf6a5e04f2ad","mcp_get_code":{"code_sha256":"0132cf6a5e04f2ad"}},{"arxiv_id":"1506.05908","paper":"/paper/deep-knowledge-tracing","title":"Deep Knowledge Tracing","date":"2015-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qqhann/KnowledgeTracing","path":"src/log.py","file_url":"https://github.com/qqhann/KnowledgeTracing/blob/HEAD/src/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8137614e705c6de1","mcp_get_code":{"code_sha256":"8137614e705c6de1"}},{"arxiv_id":"1406.6247","paper":"/paper/recurrent-models-of-visual-attention","title":"Recurrent Models of Visual Attention","date":"2014-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MiuGod0126/RAM-Paddle","path":"utils.py","file_url":"https://github.com/MiuGod0126/RAM-Paddle/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":"8f7357fe28cb2df6","mcp_get_code":{"code_sha256":"8f7357fe28cb2df6"}},{"arxiv_id":"1308.0850","paper":"/paper/generating-sequences-with-recurrent-neural","title":"Generating Sequences With Recurrent Neural Networks","date":"2013-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SamuelNguyen1998/Vietnamese_Handwriting_Recognition","path":"src/log.py","file_url":"https://github.com/SamuelNguyen1998/Vietnamese_Handwriting_Recognition/blob/HEAD/src/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce47adec051d5431","mcp_get_code":{"code_sha256":"ce47adec051d5431"}},{"arxiv_id":"ijcai2025_0372","paper":null,"title":"arXiv:ijcai2025_0372","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cwang-nus/DOL","path":"utils/logging.py","file_url":"https://github.com/cwang-nus/DOL/blob/HEAD/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"45ef37f57b414de6","mcp_get_code":{"code_sha256":"45ef37f57b414de6"}},{"arxiv_id":"aaai_6994","paper":null,"title":"arXiv:aaai_6994","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dlinzhao/JSNet","path":"utils/log_util.py","file_url":"https://github.com/dlinzhao/JSNet/blob/HEAD/utils/log_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ae2126a421125cd","mcp_get_code":{"code_sha256":"3ae2126a421125cd"}},{"arxiv_id":"aaai_29068","paper":null,"title":"arXiv:aaai_29068","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NJUyued/SoC4SS-FGVC","path":"utils.py","file_url":"https://github.com/NJUyued/SoC4SS-FGVC/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":"f134f238fbf660cc","mcp_get_code":{"code_sha256":"f134f238fbf660cc"}},{"arxiv_id":"aaai_27804","paper":null,"title":"arXiv:aaai_27804","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"DingJianhao/StoG-meets-SNN","path":"functions.py","file_url":"https://github.com/DingJianhao/StoG-meets-SNN/blob/HEAD/functions.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":"210ba8a40d178d14","mcp_get_code":{"code_sha256":"210ba8a40d178d14"}},{"arxiv_id":"aaai_20810","paper":null,"title":"arXiv:aaai_20810","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Craven-Biostat-Lab/synmod","path":"synmod/utils.py","file_url":"https://github.com/Craven-Biostat-Lab/synmod/blob/HEAD/synmod/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6dd5fbcb0be53a26","mcp_get_code":{"code_sha256":"6dd5fbcb0be53a26"}},{"arxiv_id":"aaai_16917","paper":null,"title":"arXiv:aaai_16917","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ganji15/HiGAN","path":"lib/utils.py","file_url":"https://github.com/ganji15/HiGAN/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":"e8eca4928349ff74","mcp_get_code":{"code_sha256":"e8eca4928349ff74"}},{"arxiv_id":"Xue_ULIP-2_Towards_Scalable_Multimodal_Pre-training_for_3D_Understanding_CVPR_2024_paper","paper":null,"title":"arXiv:Xue_ULIP-2_Towards_Scalable_Multimodal_Pre-training_for_3D_Understanding_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"salesforce/ULIP","path":"models/pointbert/logger.py","file_url":"https://github.com/salesforce/ULIP/blob/HEAD/models/pointbert/logger.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":"13248d61d84b9cee","mcp_get_code":{"code_sha256":"13248d61d84b9cee"}},{"arxiv_id":"Xia_GSVA_Generalized_Segmentation_via_Multimodal_Large_Language_Models_CVPR_2024_paper","paper":null,"title":"arXiv:Xia_GSVA_Generalized_Segmentation_via_Multimodal_Large_Language_Models_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LeapLabTHU/GSVA","path":"utils/logger.py","file_url":"https://github.com/LeapLabTHU/GSVA/blob/HEAD/utils/logger.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":"7e11d9ec27335f5b","mcp_get_code":{"code_sha256":"7e11d9ec27335f5b"}},{"arxiv_id":"Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper","paper":null,"title":"arXiv:Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bisno/CocoER","path":"models_sw.py","file_url":"https://github.com/bisno/CocoER/blob/HEAD/models_sw.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c5826ac713df254","mcp_get_code":{"code_sha256":"7c5826ac713df254"}},{"arxiv_id":"Pei_D2ST-Adapter_Disentangled-and-Deformable_Spatio-Temporal_Adapter_for_Few-shot_Action_Recognition_ICCV_2025_paper","paper":null,"title":"arXiv:Pei_D2ST-Adapter_Disentangled-and-Deformable_Spatio-Temporal_Adapter_for_Few-shot_Action_Recognition_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"qizhongtan/D2ST-Adapter","path":"utils/logging.py","file_url":"https://github.com/qizhongtan/D2ST-Adapter/blob/HEAD/utils/logging.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":"b6d6bcb982b768cc","mcp_get_code":{"code_sha256":"b6d6bcb982b768cc"}},{"arxiv_id":"Du_iKUN_Speak_to_Trackers_without_Retraining_CVPR_2024_paper","paper":null,"title":"arXiv:Du_iKUN_Speak_to_Trackers_without_Retraining_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dyhBUPT/iKUN","path":"utils.py","file_url":"https://github.com/dyhBUPT/iKUN/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":"003ac47d15830f47","mcp_get_code":{"code_sha256":"003ac47d15830f47"}},{"arxiv_id":"2025.emnlp-main.677","paper":null,"title":"arXiv:2025.emnlp-main.677","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"linb20/CP-GPT","path":"finetune.py","file_url":"https://github.com/linb20/CP-GPT/blob/HEAD/finetune.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"935bbefecd5812d4","mcp_get_code":{"code_sha256":"935bbefecd5812d4"}},{"arxiv_id":"2025.acl-long.1468","paper":null,"title":"arXiv:2025.acl-long.1468","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"leolee99/PIGuard","path":"util.py","file_url":"https://github.com/leolee99/PIGuard/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":"cb0a355b0a1f9d4a","mcp_get_code":{"code_sha256":"cb0a355b0a1f9d4a"}},{"arxiv_id":"2025.acl-long.10","paper":null,"title":"arXiv:2025.acl-long.10","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"THUKElab/CLEME","path":"core/utils/logging_utils.py","file_url":"https://github.com/THUKElab/CLEME/blob/HEAD/core/utils/logging_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":"66802c9a73693863","mcp_get_code":{"code_sha256":"66802c9a73693863"}},{"arxiv_id":"2024.findings-emnlp.944","paper":null,"title":"arXiv:2024.findings-emnlp.944","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jind11/HSLN-Joint-Sentence-Classification","path":"model/general_utils.py","file_url":"https://github.com/jind11/HSLN-Joint-Sentence-Classification/blob/HEAD/model/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":"75ed1ef1f8c3d22b","mcp_get_code":{"code_sha256":"75ed1ef1f8c3d22b"}},{"arxiv_id":"2024.findings-acl.847","paper":null,"title":"arXiv:2024.findings-acl.847","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mt-upc/ZeroSwot","path":"zs_st/data_prep/filter_mustc.py","file_url":"https://github.com/mt-upc/ZeroSwot/blob/HEAD/zs_st/data_prep/filter_mustc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53f39775d5832e8f","mcp_get_code":{"code_sha256":"53f39775d5832e8f"}},{"arxiv_id":"2022.naacl-main.64","paper":null,"title":"arXiv:2022.naacl-main.64","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"microsoft/MLC","path":"logger.py","file_url":"https://github.com/microsoft/MLC/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22905be7a3378c0f","mcp_get_code":{"code_sha256":"22905be7a3378c0f"}},{"arxiv_id":"2022.findings-naacl.160","paper":null,"title":"arXiv:2022.findings-naacl.160","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"malllabiisc/SGCP","path":"src/helper.py","file_url":"https://github.com/malllabiisc/SGCP/blob/HEAD/src/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":"34d6b7a9ac97b02f","mcp_get_code":{"code_sha256":"34d6b7a9ac97b02f"}},{"arxiv_id":"2022.findings-emnlp.341","paper":null,"title":"arXiv:2022.findings-emnlp.341","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"vinhsuhi/JMAC","path":"JMAC_DBPv1/modules/helper/helper.py","file_url":"https://github.com/vinhsuhi/JMAC/blob/HEAD/JMAC_DBPv1/modules/helper/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ecef8549fab0398","mcp_get_code":{"code_sha256":"4ecef8549fab0398"}},{"arxiv_id":"2022.findings-emnlp.242","paper":null,"title":"arXiv:2022.findings-emnlp.242","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"INK-USC/er-test","path":"src/utils/logging.py","file_url":"https://github.com/INK-USC/er-test/blob/HEAD/src/utils/logging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ca61f6f5c5ae515","mcp_get_code":{"code_sha256":"3ca61f6f5c5ae515"}},{"arxiv_id":"2021.naacl-srw.7","paper":null,"title":"arXiv:2021.naacl-srw.7","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tnq177/witwicky","path":"nmt/utils.py","file_url":"https://github.com/tnq177/witwicky/blob/HEAD/nmt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"039e3fb281ed1d58","mcp_get_code":{"code_sha256":"039e3fb281ed1d58"}},{"arxiv_id":"136970483","paper":null,"title":"arXiv:136970483","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"exped1230/S2-VER","path":"utils.py","file_url":"https://github.com/exped1230/S2-VER/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":"f134f238fbf660cc","mcp_get_code":{"code_sha256":"f134f238fbf660cc"}},{"arxiv_id":"136910021","paper":null,"title":"arXiv:136910021","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"UCDvision/CMSF","path":"tools.py","file_url":"https://github.com/UCDvision/CMSF/blob/HEAD/tools.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":"416b44e8cb9fc048","mcp_get_code":{"code_sha256":"416b44e8cb9fc048"}},{"arxiv_id":"136660105","paper":null,"title":"arXiv:136660105","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CR320/CoupledEmbedding","path":"utils.py","file_url":"https://github.com/CR320/CoupledEmbedding/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":"6d6db6dcbfc432df","mcp_get_code":{"code_sha256":"6d6db6dcbfc432df"}},{"arxiv_id":"136640328","paper":null,"title":"arXiv:136640328","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nutuniv/SSRL","path":"utils.py","file_url":"https://github.com/nutuniv/SSRL/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":"014d87639fcaa13f","mcp_get_code":{"code_sha256":"014d87639fcaa13f"}}]}