{"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/extract-answer-from-response","entry":"extract_answer_from_response","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":6,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2605.17937","paper":"/paper/arxiv-2605-17937","title":"BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"jensenw1/BacktestBench","path":"AutoBacktest/003_BackTest/utils.py","file_url":"https://github.com/jensenw1/BacktestBench/blob/HEAD/AutoBacktest/003_BackTest/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c7e4a4439fc947ba","mcp_get_code":{"code_sha256":"c7e4a4439fc947ba"}},{"arxiv_id":"2602.07892","paper":"/paper/arxiv-2602-07892","title":"Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"SunGL001/OGPSA","path":"eval/AIME2024.py","file_url":"https://github.com/SunGL001/OGPSA/blob/HEAD/eval/AIME2024.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e704f14bfb984d51","mcp_get_code":{"code_sha256":"e704f14bfb984d51"}},{"arxiv_id":"2602.07892","paper":"/paper/arxiv-2602-07892","title":"Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"SunGL001/OGPSA","path":"eval/GPQA.py","file_url":"https://github.com/SunGL001/OGPSA/blob/HEAD/eval/GPQA.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996c1c7807871550","mcp_get_code":{"code_sha256":"996c1c7807871550"}},{"arxiv_id":"2504.07114","paper":"/paper/chatbench-from-static-benchmarks-to-human-ai","title":"ChatBench: From Static Benchmarks to Human-AI Evaluation","date":"2025-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"serinachang5/interactive-eval","path":"qa_reasoning.py","file_url":"https://github.com/serinachang5/interactive-eval/blob/HEAD/qa_reasoning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dbd40b5329f4e864","mcp_get_code":{"code_sha256":"dbd40b5329f4e864"}},{"arxiv_id":"openreview_HJ0JFzdwUo","paper":null,"title":"arXiv:openreview_HJ0JFzdwUo","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"showlab/Code2Video","path":"src/utils.py","file_url":"https://github.com/showlab/Code2Video/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":"5152f62813877f7d","mcp_get_code":{"code_sha256":"5152f62813877f7d"}},{"arxiv_id":"2025.findings-acl.51","paper":null,"title":"arXiv:2025.findings-acl.51","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ModeEric/ORBIT-Llama","path":"orbit/evaluation/astrobench_tests.py","file_url":"https://github.com/ModeEric/ORBIT-Llama/blob/HEAD/orbit/evaluation/astrobench_tests.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":"fbc0207f0893adb4","mcp_get_code":{"code_sha256":"fbc0207f0893adb4"}}]}