{"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/stop-gradient","entry":"stop_gradient","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":4,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":1},"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":"2605.10159","paper":"/paper/arxiv-2605-10159","title":"jNO: A JAX Library for Neural Operator and Foundation Model Training","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"FhG-IISB/jNO","path":"jno/fn.py","file_url":"https://github.com/FhG-IISB/jNO/blob/HEAD/jno/fn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"EPL-2.0","inline_ok":false,"code_sha256_prefix":"93d2fc368d79e707","mcp_get_code":{"code_sha256":"93d2fc368d79e707"}},{"arxiv_id":"2406.18043","paper":"/paper/multimodal-foundation-world-models-for","title":"GenRL: Multimodal-foundation world models for generalization in embodied agents","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mazpie/genrl","path":"agent/dreamer.py","file_url":"https://github.com/mazpie/genrl/blob/HEAD/agent/dreamer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17f4154f5359d704","mcp_get_code":{"code_sha256":"17f4154f5359d704"}},{"arxiv_id":"2402.07871","paper":"/paper/scaling-laws-for-fine-grained-mixture-of","title":"Scaling Laws for Fine-Grained Mixture of Experts","date":"2024-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llm-random/llm-random","path":"lizrd/core/misc.py","file_url":"https://github.com/llm-random/llm-random/blob/HEAD/lizrd/core/misc.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"17f4154f5359d704","mcp_get_code":{"code_sha256":"17f4154f5359d704"}},{"arxiv_id":"2309.04747","paper":"/paper/when-to-learn-what-model-adaptive-data","title":"When to Learn What: Model-Adaptive Data Augmentation Curriculum","date":"2023-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JackHck/MADAug","path":"adaptive_augmentor.py","file_url":"https://github.com/JackHck/MADAug/blob/HEAD/adaptive_augmentor.py","status":"ran_fixture","verification_level":1,"contract_check":"DEP_MISSING","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f86c9dd77bb5afe7","mcp_get_code":{"code_sha256":"f86c9dd77bb5afe7"}},{"arxiv_id":"2209.12016","paper":"/paper/unsupervised-model-based-pre-training-for","title":"Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels","date":"2022-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mazpie/mastering-urlb","path":"agent/dreamer.py","file_url":"https://github.com/mazpie/mastering-urlb/blob/HEAD/agent/dreamer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17f4154f5359d704","mcp_get_code":{"code_sha256":"17f4154f5359d704"}}]}