{"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/smooth-data","entry":"smooth_data","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":2,"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":2,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2406.14537","paper":"/paper/macrohft-memory-augmented-context-aware","title":"MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZONG0004/MacroHFT","path":"preprocess/decomposition.py","file_url":"https://github.com/ZONG0004/MacroHFT/blob/HEAD/preprocess/decomposition.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f18c412f5cd4b096","mcp_get_code":{"code_sha256":"f18c412f5cd4b096"}},{"arxiv_id":"2405.19153","paper":"/paper/a-study-of-plasticity-loss-in-on-policy-deep","title":"A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awjuliani/deep-rl-plasticity","path":"shared/plotting.py","file_url":"https://github.com/awjuliani/deep-rl-plasticity/blob/HEAD/shared/plotting.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d68e1e66946f8dc3","mcp_get_code":{"code_sha256":"d68e1e66946f8dc3"}},{"arxiv_id":"1906.07906","paper":"/paper/discovery-of-physics-from-data-universal-laws","title":"Discovery of Physics from Data: Universal Laws and Discrepancies","date":"2019-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"briandesilva/discovery-of-physics-from-data","path":"code/helpers/differentiation.py","file_url":"https://github.com/briandesilva/discovery-of-physics-from-data/blob/HEAD/code/helpers/differentiation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e16b70f570f33b0e","mcp_get_code":{"code_sha256":"e16b70f570f33b0e"}},{"arxiv_id":"1811.10597","paper":"/paper/gan-dissection-visualizing-and-understanding","title":"GAN Dissection: Visualizing and Understanding Generative Adversarial Networks","date":"2018-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandonian/ganocracy","path":"gan_training/ganocracy/utils/visualizer.py","file_url":"https://github.com/alexandonian/ganocracy/blob/HEAD/gan_training/ganocracy/utils/visualizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dcc34da31f1514dd","mcp_get_code":{"code_sha256":"dcc34da31f1514dd"}},{"arxiv_id":"2025.findings-acl.669","paper":null,"title":"arXiv:2025.findings-acl.669","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"BradMcDanel/PipeSpec","path":"viz/gpu_usage_analysis.py","file_url":"https://github.com/BradMcDanel/PipeSpec/blob/HEAD/viz/gpu_usage_analysis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d23e2c477e0b3f1c","mcp_get_code":{"code_sha256":"d23e2c477e0b3f1c"}}]}