{"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/my-loss","entry":"my_loss","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":3,"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":3,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":1,"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":"2409.12293","paper":"/paper/provable-in-context-learning-of-linear","title":"In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lugroupumn/icl-ellipticpdes","path":"src/models.py","file_url":"https://github.com/lugroupumn/icl-ellipticpdes/blob/HEAD/src/models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a902739f08f51d49","mcp_get_code":{"code_sha256":"a902739f08f51d49"}},{"arxiv_id":"2203.16618","paper":"/paper/end-to-end-document-recognition-and","title":"End-to-end Document Recognition and Understanding with Dessurt","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"herobd/dessurt","path":"model/loss.py","file_url":"https://github.com/herobd/dessurt/blob/HEAD/model/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b37cbfef516510b","mcp_get_code":{"code_sha256":"9b37cbfef516510b"}},{"arxiv_id":"2006.13189","paper":"/paper/expert-supervised-reinforcement-learning-for","title":"Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation","date":"2020-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asonabend/dtr_via_surrogate_loss","path":"utils.py","file_url":"https://github.com/asonabend/dtr_via_surrogate_loss/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6b2d25b83cf9f52","mcp_get_code":{"code_sha256":"e6b2d25b83cf9f52"}},{"arxiv_id":"2006.05842","paper":"/paper/the-emergence-of-individuality-in-multi-agent","title":"The Emergence of Individuality","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiechuanjiang/eoi_on_smac","path":"model.py","file_url":"https://github.com/jiechuanjiang/eoi_on_smac/blob/HEAD/model.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b18832c07925868a","mcp_get_code":{"code_sha256":"b18832c07925868a"}},{"arxiv_id":"1804.09541","paper":"/paper/qanet-combining-local-convolution-with-global","title":"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension","date":"2018-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BangLiu/QANet-PyTorch","path":"trainer/loss.py","file_url":"https://github.com/BangLiu/QANet-PyTorch/blob/HEAD/trainer/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb4ce684e3929e65","mcp_get_code":{"code_sha256":"cb4ce684e3929e65"}}]}