{"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/euler-step","entry":"euler_step","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":1,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2608.13215","paper":"/paper/arxiv-2608-13215","title":"History-informed Lagrangian Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"yingtian22/History-informed-LNN","path":"src/models/hilnn.py","file_url":"https://github.com/yingtian22/History-informed-LNN/blob/HEAD/src/models/hilnn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c4472cd0e3b92770","mcp_get_code":{"code_sha256":"c4472cd0e3b92770"}},{"arxiv_id":"2608.08638","paper":"/paper/arxiv-2608-08638","title":"CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"OPPO-Mente-Lab/CuteTTS","path":"src/cutetts/modeling/model.py","file_url":"https://github.com/OPPO-Mente-Lab/CuteTTS/blob/HEAD/src/cutetts/modeling/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"18f99b938b96e2b6","mcp_get_code":{"code_sha256":"18f99b938b96e2b6"}},{"arxiv_id":"2307.05439","paper":"/paper/metropolis-sampling-for-constrained-diffusion","title":"Metropolis Sampling for Constrained Diffusion Models","date":"2023-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oxcsml/geomstats","path":"geomstats/integrator.py","file_url":"https://github.com/oxcsml/geomstats/blob/HEAD/geomstats/integrator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01c0388601d634d0","mcp_get_code":{"code_sha256":"01c0388601d634d0"}},{"arxiv_id":"2211.06972","paper":"/paper/experimental-study-of-neural-ode-training","title":"Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling","date":"2022-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allauzen/adaptive-step-size-neural-ode","path":"ode_utils.py","file_url":"https://github.com/allauzen/adaptive-step-size-neural-ode/blob/HEAD/ode_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"50d6dd11295c4d19","mcp_get_code":{"code_sha256":"50d6dd11295c4d19"}},{"arxiv_id":"2206.09048","paper":"/paper/iclr-2022-challenge-for-computational","title":"ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results","date":null,"month_inferred_from_arxiv_id":"2022-06","title_source":"archive","repo":"geomstats/geomstats","path":"geomstats/integrator.py","file_url":"https://github.com/geomstats/geomstats/blob/HEAD/geomstats/integrator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c96f9098ade9983","mcp_get_code":{"code_sha256":"3c96f9098ade9983"}}]}