{"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-fn","entry":"process_fn","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":0,"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":0,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":8},"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":"2503.03519","paper":"/paper/do-imagenet-trained-models-learn-shortcuts","title":"Do ImageNet-trained models learn shortcuts? The impact of frequency shortcuts on generalization","date":"2025-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nis-research/hfss","path":"hfss/LSI.py","file_url":"https://github.com/nis-research/hfss/blob/HEAD/hfss/LSI.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2223e054c99bf8d0","mcp_get_code":{"code_sha256":"2223e054c99bf8d0"}},{"arxiv_id":"2502.01456","paper":"/paper/process-reinforcement-through-implicit","title":"Process Reinforcement through Implicit Rewards","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prime-rl/prime","path":"data_preprocessing/stage1_filter.py","file_url":"https://github.com/prime-rl/prime/blob/HEAD/data_preprocessing/stage1_filter.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":"86bc2b52fc4de062","mcp_get_code":{"code_sha256":"86bc2b52fc4de062"}},{"arxiv_id":"2502.01456","paper":"/paper/process-reinforcement-through-implicit","title":"Process Reinforcement through Implicit Rewards","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prime-rl/prime","path":"data_preprocessing/stage3_merge.py","file_url":"https://github.com/prime-rl/prime/blob/HEAD/data_preprocessing/stage3_merge.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":"4dc6b4c66100580d","mcp_get_code":{"code_sha256":"4dc6b4c66100580d"}},{"arxiv_id":"2411.03284","paper":"/paper/smoa-improving-multi-agent-large-language","title":"SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"david-li0406/smoa","path":"run_alignment.py","file_url":"https://github.com/david-li0406/smoa/blob/HEAD/run_alignment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9f11a99fca0a359b","mcp_get_code":{"code_sha256":"9f11a99fca0a359b"}},{"arxiv_id":"2411.03284","paper":"/paper/smoa-improving-multi-agent-large-language","title":"SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"david-li0406/smoa","path":"run_ceb.py","file_url":"https://github.com/david-li0406/smoa/blob/HEAD/run_ceb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cecfd787af984f73","mcp_get_code":{"code_sha256":"cecfd787af984f73"}},{"arxiv_id":"2411.03284","paper":"/paper/smoa-improving-multi-agent-large-language","title":"SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"david-li0406/smoa","path":"run_mmau.py","file_url":"https://github.com/david-li0406/smoa/blob/HEAD/run_mmau.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6a8765061e98ad2","mcp_get_code":{"code_sha256":"e6a8765061e98ad2"}},{"arxiv_id":"2309.17272","paper":"/paper/enhancing-large-language-models-in-coding","title":"Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skpig/MPSC","path":"src/graph.py","file_url":"https://github.com/skpig/MPSC/blob/HEAD/src/graph.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":"c8d414f3e3e34df2","mcp_get_code":{"code_sha256":"c8d414f3e3e34df2"}},{"arxiv_id":"1703.04730","paper":"/paper/understanding-black-box-predictions-via","title":"Understanding Black-box Predictions via Influence Functions","date":"2017-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sillwood/voicemos","path":"data.py","file_url":"https://github.com/sillwood/voicemos/blob/HEAD/data.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":"ca0d20b6bf151cb8","mcp_get_code":{"code_sha256":"ca0d20b6bf151cb8"}}]}