{"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/backward-2","entry":"backward","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":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":3},"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":"2604.01472","paper":"/paper/arxiv-2604-01472","title":"The Newton-Muon Optimizer","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"KellerJordan/modded-nanogpt","path":"train_gpt_medium.py","file_url":"https://github.com/KellerJordan/modded-nanogpt/blob/HEAD/train_gpt_medium.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b144323a9c6e08cd","mcp_get_code":{"code_sha256":"b144323a9c6e08cd"}},{"arxiv_id":"2411.16085","paper":"/paper/cautious-optimizers-improving-training-with","title":"Cautious Optimizers: Improving Training with One Line of Code","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kyleliang919/c-optim","path":"nanogpt_speedrun.py","file_url":"https://github.com/kyleliang919/c-optim/blob/HEAD/nanogpt_speedrun.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b144323a9c6e08cd","mcp_get_code":{"code_sha256":"b144323a9c6e08cd"}},{"arxiv_id":"2310.13391","paper":"/paper/learning-successor-representations-with","title":"Learning Successor Features with Distributed Hebbian Temporal Memory","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vicariousinc/naturecomm_cscg","path":"chmm_actions.py","file_url":"https://github.com/vicariousinc/naturecomm_cscg/blob/HEAD/chmm_actions.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be72df6bce23ce68","mcp_get_code":{"code_sha256":"be72df6bce23ce68"}},{"arxiv_id":"2001.00735","paper":"/paper/trajectory-forecasts-in-unknown-environments","title":"Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans","date":"2020-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nachiket92/P2T","path":"models/rl.py","file_url":"https://github.com/nachiket92/P2T/blob/HEAD/models/rl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fae544a3b40bd205","mcp_get_code":{"code_sha256":"fae544a3b40bd205"}},{"arxiv_id":"1904.01774","paper":"/paper/image-generation-from-small-datasets-via","title":"Image Generation From Small Datasets via Batch Statistics Adaptation","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nogu-atsu/small-dataset-image-generation","path":"gen_models/ada_generator.py","file_url":"https://github.com/nogu-atsu/small-dataset-image-generation/blob/HEAD/gen_models/ada_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27f00fb3c1db3c49","mcp_get_code":{"code_sha256":"27f00fb3c1db3c49"}}]}