{"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-metadata","entry":"read_metadata","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":8,"n_papers_ran":3,"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":8,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":5},"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":"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":"rangehow/overleaf-mcp","path":"src/overleaf_mcp/metadata.py","file_url":"https://github.com/rangehow/overleaf-mcp/blob/HEAD/src/overleaf_mcp/metadata.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7bb60ddee022c4f9","mcp_get_code":{"code_sha256":"7bb60ddee022c4f9"}},{"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/checkpointing.py","file_url":"https://github.com/AMAP-ML/SocioReasoner/blob/HEAD/mcore_adapter/src/mcore_adapter/checkpointing.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":"ba3f7804e93d016e","mcp_get_code":{"code_sha256":"ba3f7804e93d016e"}},{"arxiv_id":"2601.05051","paper":"/paper/arxiv-2601-05051","title":"Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"sciknoworg/ald-ale-orkg-review","path":"llm-experiments/nl_query_generator_csv_onetable.py","file_url":"https://github.com/sciknoworg/ald-ale-orkg-review/blob/HEAD/llm-experiments/nl_query_generator_csv_onetable.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":"e240e56048532d2e","mcp_get_code":{"code_sha256":"e240e56048532d2e"}},{"arxiv_id":"2412.02967","paper":"/paper/a-graph-neural-network-approach-to-dispersed","title":"A Graph Neural Network Simulation of Dispersed Systems","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"rfjd/GNS-DispersedSystems","path":"gns/reading_utils.py","file_url":"https://github.com/rfjd/GNS-DispersedSystems/blob/HEAD/gns/reading_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a21c8d26e73666c4","mcp_get_code":{"code_sha256":"a21c8d26e73666c4"}},{"arxiv_id":"2411.05361","paper":"/paper/dynamic-superb-phase-2-a-collaboratively","title":"Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks","date":"2024-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dynamic-superb/dynamic-superb","path":"api/inference/utils.py","file_url":"https://github.com/dynamic-superb/dynamic-superb/blob/HEAD/api/inference/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"57a3262c8df51fb4","mcp_get_code":{"code_sha256":"57a3262c8df51fb4"}},{"arxiv_id":"2408.08926","paper":"/paper/cybench-a-framework-for-evaluating","title":"Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andyzorigin/cyber-bench","path":"run_task.py","file_url":"https://github.com/andyzorigin/cyber-bench/blob/HEAD/run_task.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":"313007004d0ed238","mcp_get_code":{"code_sha256":"313007004d0ed238"}},{"arxiv_id":"2308.13418","paper":"/paper/nougat-neural-optical-understanding-for","title":"Nougat: Neural Optical Understanding for Academic Documents","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/nougat","path":"nougat/dataset/create_index.py","file_url":"https://github.com/facebookresearch/nougat/blob/HEAD/nougat/dataset/create_index.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f21b5c9669b038e1","mcp_get_code":{"code_sha256":"f21b5c9669b038e1"}},{"arxiv_id":"1609.04747","paper":"/paper/an-overview-of-gradient-descent-optimization","title":"An overview of gradient descent optimization algorithms","date":"2016-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biocore/songbird","path":"songbird/util.py","file_url":"https://github.com/biocore/songbird/blob/HEAD/songbird/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":"34cfd615f5f453a1","mcp_get_code":{"code_sha256":"34cfd615f5f453a1"}}]}