{"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/classify","entry":"classify","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":27,"n_papers_ran":14,"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":28,"n_samples_ran":14,"n_samples_fingerprinted":9,"n_places":29,"n_places_pointer_only":8,"by_status":{"ran_honours":1,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":2,"ran":10,"unverified":14},"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":"2609.03677","paper":"/paper/arxiv-2609-03677","title":"Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"KIT-MRT/AD-Diff","path":"components/ranker.py","file_url":"https://github.com/KIT-MRT/AD-Diff/blob/HEAD/components/ranker.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7bd7b8929851dff6","mcp_get_code":{"code_sha256":"7bd7b8929851dff6"}},{"arxiv_id":"2609.00275","paper":"/paper/arxiv-2609-00275","title":"The Irreversibility Budget: Fleet-Level Risk Accounting and Admission Control for Agent Operating Systems","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"mpi-dsg/irreversibility-budget","path":"sim/traces/classify.py","file_url":"https://github.com/mpi-dsg/irreversibility-budget/blob/HEAD/sim/traces/classify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81960c37a9c658eb","mcp_get_code":{"code_sha256":"81960c37a9c658eb"}},{"arxiv_id":"2608.15286","paper":"/paper/arxiv-2608-15286","title":"No Task Fails Every Time: Why One-Shot Audits Are Structurally Blind to Agent Damage","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"shivenkk/agentrelbench","path":"m1_audit/test1_tool_inventory.py","file_url":"https://github.com/shivenkk/agentrelbench/blob/HEAD/m1_audit/test1_tool_inventory.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4b314c288935e52c","mcp_get_code":{"code_sha256":"4b314c288935e52c"}},{"arxiv_id":"2606.26803","paper":"/paper/arxiv-2606-26803","title":"From Vajrayana Tara to Bengali Baul: A Computational Study of Lexical Transmission Across Buddhist, Shakta, and Vaishnava Traditions in Bengal","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"joyboseroy/bengal-dharma-corpus","path":"patch_v3.py","file_url":"https://github.com/joyboseroy/bengal-dharma-corpus/blob/HEAD/patch_v3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3537dada33405b3e","mcp_get_code":{"code_sha256":"3537dada33405b3e"}},{"arxiv_id":"2606.10403","paper":"/paper/arxiv-2606-10403","title":"KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"naver-ai/KCSAT-ML","path":"analysis/contamination_analysis.py","file_url":"https://github.com/naver-ai/KCSAT-ML/blob/HEAD/analysis/contamination_analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"ee089fe2b6308e10","mcp_get_code":{"code_sha256":"ee089fe2b6308e10"}},{"arxiv_id":"2606.10403","paper":"/paper/arxiv-2606-10403","title":"KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"naver-ai/KCSAT-ML","path":"analysis/difficulty_signal_analysis.py","file_url":"https://github.com/naver-ai/KCSAT-ML/blob/HEAD/analysis/difficulty_signal_analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"b786586bab293243","mcp_get_code":{"code_sha256":"b786586bab293243"}},{"arxiv_id":"2605.03465","paper":"/paper/arxiv-2605-03465","title":"FINER-SQL: Boosting Small Language Models for Text-to-SQL","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"thanhdath/finer-sql","path":"db_execution/api.py","file_url":"https://github.com/thanhdath/finer-sql/blob/HEAD/db_execution/api.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dc377d73d6870e1a","mcp_get_code":{"code_sha256":"dc377d73d6870e1a"}},{"arxiv_id":"2604.27776","paper":"/paper/arxiv-2604-27776","title":"WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"HITsz-TMG/WindowsWorld","path":"show_result.py","file_url":"https://github.com/HITsz-TMG/WindowsWorld/blob/HEAD/show_result.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e0fba8134784628a","mcp_get_code":{"code_sha256":"e0fba8134784628a"}},{"arxiv_id":"2604.26052","paper":"/paper/arxiv-2604-26052","title":"From Prompt Risk to Response Risk: Paired Analysis of Safety Behavior of Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"microsoft/PairedSafety","path":"analysis/grader_error_analysis/analyze_grader_errors.py","file_url":"https://github.com/microsoft/PairedSafety/blob/HEAD/analysis/grader_error_analysis/analyze_grader_errors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"997b1fcda4c2d309","mcp_get_code":{"code_sha256":"997b1fcda4c2d309"}},{"arxiv_id":"2604.09588","paper":"/paper/arxiv-2604-09588","title":"A Multi-Anchor Architecture for Resilient Memory and Continuity","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"menonpg/soul.py","path":"hybrid_agent.py","file_url":"https://github.com/menonpg/soul.py/blob/HEAD/hybrid_agent.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"52bdf3a07ed13126","mcp_get_code":{"code_sha256":"52bdf3a07ed13126"}},{"arxiv_id":"2603.17685","paper":"/paper/arxiv-2603-17685","title":"Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints","date":"2026-03-18","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"lzqw/FLAME","path":"analysis/plot_exp37_main_figures.py","file_url":"https://github.com/lzqw/FLAME/blob/HEAD/analysis/plot_exp37_main_figures.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8f52d69d86b15b42","mcp_get_code":{"code_sha256":"8f52d69d86b15b42"}},{"arxiv_id":"2412.02529","paper":"/paper/active-learning-of-neural-population-dynamics","title":"Active learning of neural population dynamics using two-photon holographic optogenetics","date":"2024-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MouseLand/suite2p","path":"suite2p/classification/classify.py","file_url":"https://github.com/MouseLand/suite2p/blob/HEAD/suite2p/classification/classify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"1901e04637145fb3","mcp_get_code":{"code_sha256":"1901e04637145fb3"}},{"arxiv_id":"2402.11782","paper":"/paper/what-evidence-do-language-models-find","title":"What Evidence Do Language Models Find Convincing?","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlexWan0/rag-convincingness","path":"features/sentiment.py","file_url":"https://github.com/AlexWan0/rag-convincingness/blob/HEAD/features/sentiment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b770f867538c53bd","mcp_get_code":{"code_sha256":"b770f867538c53bd"}},{"arxiv_id":"2401.09266","paper":"/paper/p-2-ot-progressive-partial-optimal-transport","title":"P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rhfeiyang/ppot","path":"models/classifer.py","file_url":"https://github.com/rhfeiyang/ppot/blob/HEAD/models/classifer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d003b47d8f4e4a87","mcp_get_code":{"code_sha256":"d003b47d8f4e4a87"}},{"arxiv_id":"2401.06766","paper":"/paper/mind-your-format-towards-consistent","title":"Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements","date":"2024-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yandex-research/mind-your-format","path":"evaluate.py","file_url":"https://github.com/yandex-research/mind-your-format/blob/HEAD/evaluate.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":"aba3849d1ad68f1f","mcp_get_code":{"code_sha256":"aba3849d1ad68f1f"}},{"arxiv_id":"2312.02974","paper":"/paper/describing-differences-in-image-sets-with","title":"Describing Differences in Image Sets with Natural Language","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"understanding-visual-datasets/visdiff","path":"components/ranker.py","file_url":"https://github.com/understanding-visual-datasets/visdiff/blob/HEAD/components/ranker.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7bd7b8929851dff6","mcp_get_code":{"code_sha256":"7bd7b8929851dff6"}},{"arxiv_id":"2312.00194","paper":"/paper/robust-concept-erasure-via-kernelized-rate-1","title":"Robust Concept Erasure via Kernelized Rate-Distortion Maximization","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brcsomnath/KRaM","path":"src/utils.py","file_url":"https://github.com/brcsomnath/KRaM/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"590b4d2c85eae0a0","mcp_get_code":{"code_sha256":"590b4d2c85eae0a0"}},{"arxiv_id":"2308.01313","paper":"/paper/more-context-less-distraction-visual","title":"PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts","date":"2023-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umd-huang-lab/perceptionclip","path":"src/zero_shot_inference/perceptionclip_two_step.py","file_url":"https://github.com/umd-huang-lab/perceptionclip/blob/HEAD/src/zero_shot_inference/perceptionclip_two_step.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"707f6200db45227e","mcp_get_code":{"code_sha256":"707f6200db45227e"}},{"arxiv_id":"2209.06861","paper":"/paper/landmark-free-statistical-shape-modeling-via","title":"Landmark-free Statistical Shape Modeling via Neural Flow Deformations","date":"2022-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davecasp/flowssm","path":"utils/classifier.py","file_url":"https://github.com/davecasp/flowssm/blob/HEAD/utils/classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e3305489b1b034bd","mcp_get_code":{"code_sha256":"e3305489b1b034bd"}},{"arxiv_id":"2203.14463","paper":"/paper/large-scale-bilingual-language-image","title":"Large-scale Bilingual Language-Image Contrastive Learning","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"navervision/kelip","path":"demo/demo_zeroshot.py","file_url":"https://github.com/navervision/kelip/blob/HEAD/demo/demo_zeroshot.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":"7b87858058388425","mcp_get_code":{"code_sha256":"7b87858058388425"}},{"arxiv_id":"2112.05224","paper":"/paper/spinning-language-models-for-propaganda-as-a","title":"Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ebagdasa/propaganda_as_a_service","path":"examples/pytorch/translation/run_translation.py","file_url":"https://github.com/ebagdasa/propaganda_as_a_service/blob/HEAD/examples/pytorch/translation/run_translation.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"67d3837326021806","mcp_get_code":{"code_sha256":"67d3837326021806"}},{"arxiv_id":"2008.06376","paper":"/paper/mlm-a-benchmark-dataset-for-multitask","title":"MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities","date":"2020-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GOALCLEOPATRA/MLM","path":"utils.py","file_url":"https://github.com/GOALCLEOPATRA/MLM/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":"fc1deb03f7852eb2","mcp_get_code":{"code_sha256":"fc1deb03f7852eb2"}},{"arxiv_id":"2006.12101","paper":"/paper/p3gm-private-high-dimensional-data-release","title":"P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsubasat/P3GM","path":"ml_task/mnist_classification.py","file_url":"https://github.com/tsubasat/P3GM/blob/HEAD/ml_task/mnist_classification.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b3372fdcfeff79d","mcp_get_code":{"code_sha256":"2b3372fdcfeff79d"}},{"arxiv_id":"2006.12101","paper":"/paper/p3gm-private-high-dimensional-data-release","title":"P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsubasat/P3GM","path":"ml_task/tabledata_classification.py","file_url":"https://github.com/tsubasat/P3GM/blob/HEAD/ml_task/tabledata_classification.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6da71f6fed8df80b","mcp_get_code":{"code_sha256":"6da71f6fed8df80b"}},{"arxiv_id":"1907.05982","paper":"/paper/learning-complex-basis-functions-for","title":"Learning Complex Basis Functions for Invariant Representations of Audio","date":"2019-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SonyCSLParis/cae-invar","path":"convert.py","file_url":"https://github.com/SonyCSLParis/cae-invar/blob/HEAD/convert.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"cb2dd797c889d65b","mcp_get_code":{"code_sha256":"cb2dd797c889d65b"}},{"arxiv_id":"1606.03657","paper":"/paper/infogan-interpretable-representation-learning","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","date":"2016-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"reihaneh-torkzadehmahani/DP-CGAN","path":"DP_CGAN/dp_conditional_gan_mnist/DP_CGAN_MomentAcc.py","file_url":"https://github.com/reihaneh-torkzadehmahani/DP-CGAN/blob/HEAD/DP_CGAN/dp_conditional_gan_mnist/DP_CGAN_MomentAcc.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"effa600d353bed01","mcp_get_code":{"code_sha256":"effa600d353bed01"}},{"arxiv_id":"1512.00567","paper":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DanishShah/DeepDiagnosis","path":"Web/Backend/predict.py","file_url":"https://github.com/DanishShah/DeepDiagnosis/blob/HEAD/Web/Backend/predict.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d804546ecbde837","mcp_get_code":{"code_sha256":"1d804546ecbde837"}},{"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":"models/soc/soc_utils.py","file_url":"https://github.com/NJUyued/SoC4SS-FGVC/blob/HEAD/models/soc/soc_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46c00ea45d5825c1","mcp_get_code":{"code_sha256":"46c00ea45d5825c1"}},{"arxiv_id":"2024.findings-emnlp.130","paper":null,"title":"arXiv:2024.findings-emnlp.130","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wojciechowskiofficial/FLEX","path":"src/utils.py","file_url":"https://github.com/wojciechowskiofficial/FLEX/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":"383aa52de4a5faf8","mcp_get_code":{"code_sha256":"383aa52de4a5faf8"}}]}