{"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/get-batch-loss","entry":"get_batch_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":8,"n_papers_ran":5,"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":8,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2606.27683","paper":"/paper/arxiv-2606-27683","title":"CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"DGL-codes/CBD","path":"uld/tofuutil/data_module.py","file_url":"https://github.com/DGL-codes/CBD/blob/HEAD/uld/tofuutil/data_module.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44dd25d964127b38","mcp_get_code":{"code_sha256":"44dd25d964127b38"}},{"arxiv_id":"2510.02392","paper":"/paper/arxiv-2510-02392","title":"KnowledgeSmith: Uncovering Knowledge Updating in LLMs with Model Editing and Unlearning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"AIFrontierLab/KnowledgeSmith","path":"training/unlearn/baselines/src/utils.py","file_url":"https://github.com/AIFrontierLab/KnowledgeSmith/blob/HEAD/training/unlearn/baselines/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"373cbe86765cc7cc","mcp_get_code":{"code_sha256":"373cbe86765cc7cc"}},{"arxiv_id":"2506.01339","paper":"/paper/invariance-makes-llm-unlearning-resilient","title":"Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning","date":"2025-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OPTML-Group/Unlearn-ILU","path":"src/unlearn/utils.py","file_url":"https://github.com/OPTML-Group/Unlearn-ILU/blob/HEAD/src/unlearn/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"442bc919060e0f2b","mcp_get_code":{"code_sha256":"442bc919060e0f2b"}},{"arxiv_id":"2502.14829","paper":"/paper/measuring-faithfulness-of-chains-of-thought","title":"Measuring Faithfulness of Chains of Thought by Unlearning Reasoning Steps","date":"2025-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"44dd25d964127b38","mcp_get_code":{"code_sha256":"44dd25d964127b38"}},{"arxiv_id":"2502.11190","paper":"/paper/relearn-unlearning-via-learning-for-large","title":"ReLearn: Unlearning via Learning for Large Language Models","date":"2025-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/unlearn","path":"baselines/src/utils.py","file_url":"https://github.com/zjunlp/unlearn/blob/HEAD/baselines/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":"373cbe86765cc7cc","mcp_get_code":{"code_sha256":"373cbe86765cc7cc"}},{"arxiv_id":"2409.11844","paper":"/paper/meow-memory-supervised-llm-unlearning-via","title":"MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carol-gutianle/meow","path":"meow/utils.py","file_url":"https://github.com/carol-gutianle/meow/blob/HEAD/meow/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"442bc919060e0f2b","mcp_get_code":{"code_sha256":"442bc919060e0f2b"}},{"arxiv_id":"2307.13702","paper":"/paper/measuring-faithfulness-in-chain-of-thought","title":"Measuring Faithfulness in Chain-of-Thought Reasoning","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"technion-cs-nlp/parametric-faithfulness","path":"unlearn.py","file_url":"https://github.com/technion-cs-nlp/parametric-faithfulness/blob/HEAD/unlearn.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"44dd25d964127b38","mcp_get_code":{"code_sha256":"44dd25d964127b38"}},{"arxiv_id":"1906.04225","paper":"/paper/label-agnostic-sequence-labeling-by-copying","title":"Label-Agnostic Sequence Labeling by Copying Nearest Neighbors","date":"2019-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"swiseman/neighbor-tagging","path":"train_words.py","file_url":"https://github.com/swiseman/neighbor-tagging/blob/HEAD/train_words.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16a42c9a9ed5df3c","mcp_get_code":{"code_sha256":"16a42c9a9ed5df3c"}}]}