{"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/work","entry":"work","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":9,"n_papers_ran":7,"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":4,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":2},"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":"2502.04773","paper":"/paper/an-extended-benchmarking-of-multi-agent","title":"An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks","date":"2025-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ailabdsunipi/pymarlzooplus","path":"pymarlzooplus/search.py","file_url":"https://github.com/ailabdsunipi/pymarlzooplus/blob/HEAD/pymarlzooplus/search.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"2410.08540","paper":"/paper/kaleidoscope-learnable-masks-for","title":"Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LXXXXR/Kaleidoscope","path":"Kalei_MPE/src/search.py","file_url":"https://github.com/LXXXXR/Kaleidoscope/blob/HEAD/Kalei_MPE/src/search.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":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"2410.03997","paper":null,"title":"arXiv:2410.03997","date":null,"month_inferred_from_arxiv_id":"2024-10","title_source":null,"repo":"paulzyzy/YOLO-MARL","path":"YOLO-MARL/src/search.py","file_url":"https://github.com/paulzyzy/YOLO-MARL/blob/HEAD/YOLO-MARL/src/search.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"2309.00071","paper":"/paper/yarn-efficient-context-window-extension-of","title":"YaRN: Efficient Context Window Extension of Large Language Models","date":"2023-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qwenlm/qwq","path":"eval/eval/math_opensource.py","file_url":"https://github.com/qwenlm/qwq/blob/HEAD/eval/eval/math_opensource.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":"97dd1cf2a21b437c","mcp_get_code":{"code_sha256":"97dd1cf2a21b437c"}},{"arxiv_id":"2212.05331","paper":"/paper/effects-of-spectral-normalization-in-multi","title":"Effects of Spectral Normalization in Multi-agent Reinforcement Learning","date":"2022-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kinalmehta/epymarl_spectral","path":"src_spectral/search.py","file_url":"https://github.com/kinalmehta/epymarl_spectral/blob/HEAD/src_spectral/search.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":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"2006.07869","paper":"/paper/comparative-evaluation-of-multi-agent-deep","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dtabas/epymarl","path":"src/search.py","file_url":"https://github.com/dtabas/epymarl/blob/HEAD/src/search.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"1912.01321","paper":"/paper/less-is-better-unweighted-data-subsampling","title":"Less Is Better: Unweighted Data Subsampling via Influence Function","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RyanWangZf/Influence_Subsampling","path":"process_data.py","file_url":"https://github.com/RyanWangZf/Influence_Subsampling/blob/HEAD/process_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9278c6ddf04cba8","mcp_get_code":{"code_sha256":"b9278c6ddf04cba8"}},{"arxiv_id":"1910.07483","paper":"/paper/maven-multi-agent-variational-exploration","title":"MAVEN: Multi-Agent Variational Exploration","date":"2019-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chandar-lab/coe","path":"src/search.py","file_url":"https://github.com/chandar-lab/coe/blob/HEAD/src/search.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":"930812c3b06fc858","mcp_get_code":{"code_sha256":"930812c3b06fc858"}},{"arxiv_id":"1906.00695","paper":"/paper/190600695","title":"Continual learning with hypernetworks","date":"2019-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rvl-lab-utoronto/HyperCRL","path":"scripts/run_pusher.py","file_url":"https://github.com/rvl-lab-utoronto/HyperCRL/blob/HEAD/scripts/run_pusher.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3263fbc26d935680","mcp_get_code":{"code_sha256":"3263fbc26d935680"}}]}