{"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/load-trajectories","entry":"load_trajectories","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":6,"n_papers_ran":1,"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":8,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":7},"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":"2605.30880","paper":"/paper/arxiv-2605-30880","title":"PatchWorld: Gradient-Free Optimization of Executable World Models for Agent Environments","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"HKBU-KnowComp/PatchWorld","path":"patchworld/worldmodel_data.py","file_url":"https://github.com/HKBU-KnowComp/PatchWorld/blob/HEAD/patchworld/worldmodel_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df272630397f75fe","mcp_get_code":{"code_sha256":"df272630397f75fe"}},{"arxiv_id":"2602.10793","paper":"/paper/arxiv-2602-10793","title":"Semi-Supervised Cross-Domain Imitation Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Div-Infinity/IQ-Learn","path":"iq_learn/dataset/expert_dataset.py","file_url":"https://github.com/Div-Infinity/IQ-Learn/blob/HEAD/iq_learn/dataset/expert_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7d10ded5ef8029af","mcp_get_code":{"code_sha256":"7d10ded5ef8029af"}},{"arxiv_id":"2602.06923","paper":"/paper/arxiv-2602-06923","title":"From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"KindXiaoming/newton-kepler","path":"kepler.py","file_url":"https://github.com/KindXiaoming/newton-kepler/blob/HEAD/kepler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e75836153ab7e442","mcp_get_code":{"code_sha256":"e75836153ab7e442"}},{"arxiv_id":"2602.06923","paper":"/paper/arxiv-2602-06923","title":"From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"KindXiaoming/newton-kepler","path":"kepler_cv_blocksize.py","file_url":"https://github.com/KindXiaoming/newton-kepler/blob/HEAD/kepler_cv_blocksize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f06d7888d73cea85","mcp_get_code":{"code_sha256":"f06d7888d73cea85"}},{"arxiv_id":"2503.14443","paper":"/paper/envbench-a-benchmark-for-automated","title":"EnvBench: A Benchmark for Automated Environment Setup","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JetBrains-Research/EnvBench","path":"env_setup_utils/analysis/analysis_utils.py","file_url":"https://github.com/JetBrains-Research/EnvBench/blob/HEAD/env_setup_utils/analysis/analysis_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fb8ef64f77241e01","mcp_get_code":{"code_sha256":"fb8ef64f77241e01"}},{"arxiv_id":"2503.14443","paper":"/paper/envbench-a-benchmark-for-automated","title":"EnvBench: A Benchmark for Automated Environment Setup","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JetBrains-Research/EnvBench","path":"env_setup_utils/analysis/traj_viewer.py","file_url":"https://github.com/JetBrains-Research/EnvBench/blob/HEAD/env_setup_utils/analysis/traj_viewer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d632320966d776a","mcp_get_code":{"code_sha256":"6d632320966d776a"}},{"arxiv_id":"2312.11598","paper":"/paper/skilldiffuser-interpretable-hierarchical","title":"SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution","date":"2023-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liang-ZX/SkillDiffuser","path":"skilldiffuser/hrl/expert_dataset.py","file_url":"https://github.com/Liang-ZX/SkillDiffuser/blob/HEAD/skilldiffuser/hrl/expert_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f2bf1c69ffe9deea","mcp_get_code":{"code_sha256":"f2bf1c69ffe9deea"}},{"arxiv_id":"2307.07205","paper":"/paper/multimodal-motion-conditioned-diffusion-model","title":"Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection","date":"2023-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aleflabo/MoCoDAD","path":"utils/data.py","file_url":"https://github.com/aleflabo/MoCoDAD/blob/HEAD/utils/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7e3b54a8686d2b2","mcp_get_code":{"code_sha256":"f7e3b54a8686d2b2"}}]}