{"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/process-dataframe","entry":"process_dataframe","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":5,"n_papers_ran":1,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":4},"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":"2602.10685","paper":"/paper/arxiv-2602-10685","title":"Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"amendb/CollaborationMetrics","path":"Evaluation/AlgorithmAnalizerLegendarium.py","file_url":"https://github.com/amendb/CollaborationMetrics/blob/HEAD/Evaluation/AlgorithmAnalizerLegendarium.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"65c9c893e779da9f","mcp_get_code":{"code_sha256":"65c9c893e779da9f"}},{"arxiv_id":"2510.19040","paper":"/paper/arxiv-2510-19040","title":"Empowering Decision Trees via Shape Function Branching","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"optimal-uoft/Empowering-DTs-via-Shape-Functions","path":"src/data_utils.py","file_url":"https://github.com/optimal-uoft/Empowering-DTs-via-Shape-Functions/blob/HEAD/src/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"423116625a3e6ca4","mcp_get_code":{"code_sha256":"423116625a3e6ca4"}},{"arxiv_id":"2411.00056","paper":null,"title":"arXiv:2411.00056","date":null,"month_inferred_from_arxiv_id":"2024-11","title_source":null,"repo":"DarianRodriguez/NegVerse","path":"negator/PEFT/data_preprocess.py","file_url":"https://github.com/DarianRodriguez/NegVerse/blob/HEAD/negator/PEFT/data_preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"08fd794377ef72a1","mcp_get_code":{"code_sha256":"08fd794377ef72a1"}},{"arxiv_id":"2410.21943","paper":"/paper/beyond-text-optimizing-rag-with-multimodal","title":"Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"riedlerm/multimodal_rag_for_industry","path":"src/question_answering/rag/run_multimodal_rag.py","file_url":"https://github.com/riedlerm/multimodal_rag_for_industry/blob/HEAD/src/question_answering/rag/run_multimodal_rag.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":"8fad12c807b0493a","mcp_get_code":{"code_sha256":"8fad12c807b0493a"}},{"arxiv_id":"2107.07502","paper":"/paper/multibench-multiscale-benchmarks-for","title":"MultiBench: Multiscale Benchmarks for Multimodal Representation Learning","date":"2021-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mit-lcp/mimic-code","path":"mimic-iv/buildmimic/sqlite/import.py","file_url":"https://github.com/mit-lcp/mimic-code/blob/HEAD/mimic-iv/buildmimic/sqlite/import.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8457a015d3002b75","mcp_get_code":{"code_sha256":"8457a015d3002b75"}}]}