{"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/r2-score","entry":"r2_score","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":7,"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":6,"n_samples_ran":3,"n_samples_fingerprinted":2,"n_places":7,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"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":"2607.19376","paper":"/paper/arxiv-2607-19376","title":"Refnd: Preventing Data Leakage in Relational Datasets","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"anthol42/QMAP","path":"QMAP/src/qmap/benchmark/dataset/metrics.py","file_url":"https://github.com/anthol42/QMAP/blob/HEAD/QMAP/src/qmap/benchmark/dataset/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fb3ad267dcf31bbd","mcp_get_code":{"code_sha256":"fb3ad267dcf31bbd"}},{"arxiv_id":"2601.22371","paper":"/paper/arxiv-2601-22371","title":"FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Mohamedelrefaie/DrivAerNet","path":"DeepSurrogates/train_RegPointNet.py","file_url":"https://github.com/Mohamedelrefaie/DrivAerNet/blob/HEAD/DeepSurrogates/train_RegPointNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ff6a277b3a043f21","mcp_get_code":{"code_sha256":"ff6a277b3a043f21"}},{"arxiv_id":"2512.00239","paper":"/paper/arxiv-2512-00239","title":"Self-Supervised Dynamical System Representations for Physiological Time-Series","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"arsedler9/lfads-torch","path":"lfads_torch/metrics.py","file_url":"https://github.com/arsedler9/lfads-torch/blob/HEAD/lfads_torch/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a27593d38113b6a5","mcp_get_code":{"code_sha256":"a27593d38113b6a5"}},{"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":"analyze_helpers.py","file_url":"https://github.com/awjuliani/deep-rl-plasticity/blob/HEAD/analyze_helpers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3541cbfdecc9d36f","mcp_get_code":{"code_sha256":"3541cbfdecc9d36f"}},{"arxiv_id":"2310.05327","paper":"/paper/provable-compositional-generalization-for","title":"Provable Compositional Generalization for Object-Centric Learning","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brendel-group/objects-compositional-generalization","path":"src/metrics.py","file_url":"https://github.com/brendel-group/objects-compositional-generalization/blob/HEAD/src/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aec878a7da0179b8","mcp_get_code":{"code_sha256":"aec878a7da0179b8"}},{"arxiv_id":"2212.03771","paper":"/paper/expressive-architectures-enhance","title":"Expressive architectures enhance interpretability of dynamics-based neural population models","date":"2022-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snel-repo/expressive-latent-dynamics-paper","path":"paper_src/metrics.py","file_url":"https://github.com/snel-repo/expressive-latent-dynamics-paper/blob/HEAD/paper_src/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"93a4ed3809fa0894","mcp_get_code":{"code_sha256":"93a4ed3809fa0894"}},{"arxiv_id":"2109.04463","paper":"/paper/neural-latents-benchmark-21-evaluating-latent","title":"Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity","date":"2021-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kabirdabholkar/nlb_tools_fewshot","path":"nlb_tools/metrics.py","file_url":"https://github.com/kabirdabholkar/nlb_tools_fewshot/blob/HEAD/nlb_tools/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a27593d38113b6a5","mcp_get_code":{"code_sha256":"a27593d38113b6a5"}}]}