{"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/read","entry":"read","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":34,"n_papers_ran":7,"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":31,"n_samples_ran":8,"n_samples_fingerprinted":0,"n_places":35,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":6,"unverified":23},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2609.03430","paper":"/paper/arxiv-2609-03430","title":"Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"SalesforceAIResearch/Random-Attention","path":"figures/plot_throughput.py","file_url":"https://github.com/SalesforceAIResearch/Random-Attention/blob/HEAD/figures/plot_throughput.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":"94c268f7736d16d7","mcp_get_code":{"code_sha256":"94c268f7736d16d7"}},{"arxiv_id":"2608.24460","paper":"/paper/arxiv-2608-24460","title":"Shortcut Before Circuit: Document Statistics Time In-Context Conflict Resolution","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"lyj20071013/Shortcut-Before-Circuit","path":"fig_traj.py","file_url":"https://github.com/lyj20071013/Shortcut-Before-Circuit/blob/HEAD/fig_traj.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":"d3e77f4daa41f29e","mcp_get_code":{"code_sha256":"d3e77f4daa41f29e"}},{"arxiv_id":"2607.17765","paper":"/paper/arxiv-2607-17765","title":"FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"graphuofm/FIFA2026LLM","path":"src/build_dataset.py","file_url":"https://github.com/graphuofm/FIFA2026LLM/blob/HEAD/src/build_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e8469856b83f0bf2","mcp_get_code":{"code_sha256":"e8469856b83f0bf2"}},{"arxiv_id":"2606.28455","paper":"/paper/arxiv-2606-28455","title":"Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Structure in Passive Object-State World Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"lysea8282/event-conditioned-world-model-diagnostics","path":"src/figures/generate_figure1_three_seed_release.py","file_url":"https://github.com/lysea8282/event-conditioned-world-model-diagnostics/blob/HEAD/src/figures/generate_figure1_three_seed_release.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"eed96e37da48d1d3","mcp_get_code":{"code_sha256":"eed96e37da48d1d3"}},{"arxiv_id":"2603.22213","paper":"/paper/arxiv-2603-22213","title":"SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"Tangkexian/SPA","path":"src/utils_tools/python_utils.py","file_url":"https://github.com/Tangkexian/SPA/blob/HEAD/src/utils_tools/python_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cc00af6d58514f4","mcp_get_code":{"code_sha256":"0cc00af6d58514f4"}},{"arxiv_id":"2505.12371","paper":"/paper/medagentboard-benchmarking-multi-agent","title":"MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks","date":"2025-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HAIRLAB/Pre_Surv_COVID_19","path":"utils.py","file_url":"https://github.com/HAIRLAB/Pre_Surv_COVID_19/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":"bd1a6c1715adc6c4","mcp_get_code":{"code_sha256":"bd1a6c1715adc6c4"}},{"arxiv_id":"2501.15893","paper":"/paper/benchmarking-quantum-reinforcement-learning","title":"Benchmarking Quantum Reinforcement Learning","date":"2025-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicomeyer96/qrl-benchmark","path":"plot/helper.py","file_url":"https://github.com/nicomeyer96/qrl-benchmark/blob/HEAD/plot/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":"660585294c4932c4","mcp_get_code":{"code_sha256":"660585294c4932c4"}},{"arxiv_id":"2412.01769","paper":"/paper/commit0-library-generation-from-scratch","title":"Commit0: Library Generation from Scratch","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"commit-0/commit0","path":"commit0/harness/get_pytest_ids.py","file_url":"https://github.com/commit-0/commit0/blob/HEAD/commit0/harness/get_pytest_ids.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ca3d74fec5f16d07","mcp_get_code":{"code_sha256":"ca3d74fec5f16d07"}},{"arxiv_id":"2411.06171","paper":"/paper/seekr-selective-attention-guided-knowledge","title":"SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models","date":"2024-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinghan1he/SEEKR","path":"show_results.py","file_url":"https://github.com/jinghan1he/SEEKR/blob/HEAD/show_results.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":"6e4c7308dedfbfb6","mcp_get_code":{"code_sha256":"6e4c7308dedfbfb6"}},{"arxiv_id":"2409.07431","paper":"/paper/synthetic-continued-pretraining","title":"Synthetic continued pretraining","date":"2024-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zitongyang/synthetic_continued_pretraining","path":"utils/python_utils.py","file_url":"https://github.com/zitongyang/synthetic_continued_pretraining/blob/HEAD/utils/python_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":"0cc00af6d58514f4","mcp_get_code":{"code_sha256":"0cc00af6d58514f4"}},{"arxiv_id":"2402.17300","paper":"/paper/voco-a-simple-yet-effective-volume","title":"VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luffy03/voco","path":"Finetune/AbdomenAtlas/check.py","file_url":"https://github.com/luffy03/voco/blob/HEAD/Finetune/AbdomenAtlas/check.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":"d78a609520e9a2b5","mcp_get_code":{"code_sha256":"d78a609520e9a2b5"}},{"arxiv_id":"2402.01729","paper":"/paper/contextualization-distillation-from-large","title":"Contextualization Distillation from Large Language Model for Knowledge Graph Completion","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"David-Li0406/Contextulization-Distillation","path":"CSProm-KG/helper.py","file_url":"https://github.com/David-Li0406/Contextulization-Distillation/blob/HEAD/CSProm-KG/helper.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d619daabb614dbe4","mcp_get_code":{"code_sha256":"d619daabb614dbe4"}},{"arxiv_id":"2402.01729","paper":"/paper/contextualization-distillation-from-large","title":"Contextualization Distillation from Large Language Model for Knowledge Graph Completion","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"David-Li0406/Contextulization-Distillation","path":"KG-S2S/helper.py","file_url":"https://github.com/David-Li0406/Contextulization-Distillation/blob/HEAD/KG-S2S/helper.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1214bdb8f4557e4","mcp_get_code":{"code_sha256":"e1214bdb8f4557e4"}},{"arxiv_id":"2312.02244","paper":"/paper/geometrically-driven-aggregation-for-zero","title":"Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gfmei/GeoZe","path":"demo/pack_space.py","file_url":"https://github.com/gfmei/GeoZe/blob/HEAD/demo/pack_space.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b8321b24e059be4","mcp_get_code":{"code_sha256":"2b8321b24e059be4"}},{"arxiv_id":"2310.10704","paper":"/paper/optimized-tokenization-for-transcribed-error","title":"Optimized Tokenization for Transcribed Error Correction","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/sentencepiece","path":"lite/amalgamate.py","file_url":"https://github.com/google/sentencepiece/blob/HEAD/lite/amalgamate.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":"d5534ccd264ea291","mcp_get_code":{"code_sha256":"d5534ccd264ea291"}},{"arxiv_id":"2310.05386","paper":"/paper/constructing-and-compressing-global-moment","title":"Constructing and Compressing Global Moment Descriptors from Local Atomic Environments","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":"archive","repo":"atomisticnet/aenet-python","path":"src/aenet/config.py","file_url":"https://github.com/atomisticnet/aenet-python/blob/HEAD/src/aenet/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MPL-2.0","inline_ok":false,"code_sha256_prefix":"c042c53db62e2b46","mcp_get_code":{"code_sha256":"c042c53db62e2b46"}},{"arxiv_id":"2306.12517","paper":"/paper/ffcv-accelerating-training-by-removing-data-1","title":"FFCV: Accelerating Training by Removing Data Bottlenecks","date":"2023-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"libffcv/ffcv","path":"ffcv/libffcv.py","file_url":"https://github.com/libffcv/ffcv/blob/HEAD/ffcv/libffcv.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":"d0961aca2f9c9628","mcp_get_code":{"code_sha256":"d0961aca2f9c9628"}},{"arxiv_id":"2206.09166","paper":"/paper/nas-bench-graph-benchmarking-graph-neural","title":"NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search","date":"2022-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thumnlab/nas-bench-graph","path":"nas-bench-graph/readbench.py","file_url":"https://github.com/thumnlab/nas-bench-graph/blob/HEAD/nas-bench-graph/readbench.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":"233f072e68a2d75c","mcp_get_code":{"code_sha256":"233f072e68a2d75c"}},{"arxiv_id":"2205.14620","paper":"/paper/ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltkong218/IFRNet","path":"utils.py","file_url":"https://github.com/ltkong218/IFRNet/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":"bbbedcca6e1544a7","mcp_get_code":{"code_sha256":"bbbedcca6e1544a7"}},{"arxiv_id":"2204.08887","paper":"/paper/cross-lingual-phrase-retrieval","title":"Cross-Lingual Phrase Retrieval","date":"2022-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"artetxem/vecmap","path":"map_embeddings.py","file_url":"https://github.com/artetxem/vecmap/blob/HEAD/map_embeddings.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":"f44faa56f13d969d","mcp_get_code":{"code_sha256":"f44faa56f13d969d"}},{"arxiv_id":"2109.04556","paper":"/paper/subword-mapping-and-anchoring-across","title":"Subword Mapping and Anchoring across Languages","date":"2021-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"georgevern/smala","path":"extract_alignments.py","file_url":"https://github.com/georgevern/smala/blob/HEAD/extract_alignments.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":"781409d243fb95c0","mcp_get_code":{"code_sha256":"781409d243fb95c0"}},{"arxiv_id":"2104.10442","paper":"/paper/fourier-contour-embedding-for-arbitrary","title":"Fourier Contour Embedding for Arbitrary-Shaped Text Detection","date":"2021-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiminzhang0830/FCENet_Paddle","path":"PPOCRLabel/PPOCRLabel.py","file_url":"https://github.com/zhiminzhang0830/FCENet_Paddle/blob/HEAD/PPOCRLabel/PPOCRLabel.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":"14b9c019942ef1b5","mcp_get_code":{"code_sha256":"14b9c019942ef1b5"}},{"arxiv_id":"2009.09941","paper":"/paper/pp-ocr-a-practical-ultra-lightweight-ocr","title":"PP-OCR: A Practical Ultra Lightweight OCR System","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mushroomcat9998/PaddleOCR","path":"PPOCRLabel/PPOCRLabel.py","file_url":"https://github.com/Mushroomcat9998/PaddleOCR/blob/HEAD/PPOCRLabel/PPOCRLabel.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":"14b9c019942ef1b5","mcp_get_code":{"code_sha256":"14b9c019942ef1b5"}},{"arxiv_id":"2007.07542","paper":"/paper/robustscanner-dynamically-enhancing","title":"RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smilelite/RobustScanner.paddle","path":"PPOCRLabel/PPOCRLabel.py","file_url":"https://github.com/smilelite/RobustScanner.paddle/blob/HEAD/PPOCRLabel/PPOCRLabel.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":"14b9c019942ef1b5","mcp_get_code":{"code_sha256":"14b9c019942ef1b5"}},{"arxiv_id":"2006.01938","paper":"/paper/nurse-is-closer-to-woman-than-surgeon","title":"Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings","date":"2020-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TimeTraveller-San/RAN-Debias","path":"src/combine.py","file_url":"https://github.com/TimeTraveller-San/RAN-Debias/blob/HEAD/src/combine.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16667359f4cb8fda","mcp_get_code":{"code_sha256":"16667359f4cb8fda"}},{"arxiv_id":"1911.11763","paper":"/paper/superglue-learning-feature-matching-with","title":"SuperGlue: Learning Feature Matching with Graph Neural Networks","date":"2019-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nodarsensor/medium-keypoint-autocal","path":"keypoint_utils.py","file_url":"https://github.com/nodarsensor/medium-keypoint-autocal/blob/HEAD/keypoint_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29dea47dffb92051","mcp_get_code":{"code_sha256":"29dea47dffb92051"}},{"arxiv_id":"1905.08171","paper":"/paper/semi-supervised-learning-by-augmented","title":"Semi-Supervised Learning by Augmented Distribution Alignment","date":"2019-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qinenergy/adanet","path":"convlarge/dataset_utils.py","file_url":"https://github.com/qinenergy/adanet/blob/HEAD/convlarge/dataset_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"643612c2c781c32d","mcp_get_code":{"code_sha256":"643612c2c781c32d"}},{"arxiv_id":"1905.05901","paper":"/paper/learning-what-and-where-to-transfer","title":"Learning What and Where to Transfer","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alinlab/L2T-ww","path":"cub200.py","file_url":"https://github.com/alinlab/L2T-ww/blob/HEAD/cub200.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0df0022a8f219eba","mcp_get_code":{"code_sha256":"0df0022a8f219eba"}},{"arxiv_id":"1903.07824","paper":"/paper/compressed-sensing-from-research-to-clinical","title":"Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning","date":"2019-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MRSRL/dl-cs","path":"utils/cfl.py","file_url":"https://github.com/MRSRL/dl-cs/blob/HEAD/utils/cfl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07bd7b2d312db7f7","mcp_get_code":{"code_sha256":"07bd7b2d312db7f7"}},{"arxiv_id":"1802.08705","paper":"/paper/stripstream-integrating-symbolic-planners-and","title":"PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning","date":"2018-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingxixu/pddlstream","path":"pddlstream/utils.py","file_url":"https://github.com/jingxixu/pddlstream/blob/HEAD/pddlstream/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"37d549f907d5280f","mcp_get_code":{"code_sha256":"37d549f907d5280f"}},{"arxiv_id":"1703.06103","paper":"/paper/modeling-relational-data-with-graph","title":"Modeling Relational Data with Graph Convolutional Networks","date":"2017-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MichSchli/RelationPrediction","path":"code/common/settings_reader.py","file_url":"https://github.com/MichSchli/RelationPrediction/blob/HEAD/code/common/settings_reader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aecde0e9a66b3279","mcp_get_code":{"code_sha256":"aecde0e9a66b3279"}},{"arxiv_id":"1409.1556","paper":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vwegn/dm","path":"flow_IO.py","file_url":"https://github.com/vwegn/dm/blob/HEAD/flow_IO.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5fb26ab710fd8916","mcp_get_code":{"code_sha256":"5fb26ab710fd8916"}},{"arxiv_id":"aaai_26777","paper":null,"title":"arXiv:aaai_26777","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"heartexlabs/labelImg","path":"labelImg.py","file_url":"https://github.com/heartexlabs/labelImg/blob/HEAD/labelImg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d75e03da58918322","mcp_get_code":{"code_sha256":"d75e03da58918322"}},{"arxiv_id":"aaai_17874","paper":null,"title":"arXiv:aaai_17874","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hbaniecki/Pre-Surv-COVID-19","path":"utils.py","file_url":"https://github.com/hbaniecki/Pre-Surv-COVID-19/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":"bd1a6c1715adc6c4","mcp_get_code":{"code_sha256":"bd1a6c1715adc6c4"}},{"arxiv_id":"aaai_17676","paper":null,"title":"arXiv:aaai_17676","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/AdversarialTaboo","path":"chat_utils.py","file_url":"https://github.com/thunlp/AdversarialTaboo/blob/HEAD/chat_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d437a851d9ff39a1","mcp_get_code":{"code_sha256":"d437a851d9ff39a1"}}]}