{"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/log-sinkhorn","entry":"log_sinkhorn","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":6,"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":5,"n_samples_fingerprinted":4,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":3,"ran":1,"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":"2606.00514","paper":"/paper/arxiv-2606-00514","title":"Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Genentech/semantic-transport-generation","path":"semot/loss.py","file_url":"https://github.com/Genentech/semantic-transport-generation/blob/HEAD/semot/loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48b1da01748397a1","mcp_get_code":{"code_sha256":"48b1da01748397a1"}},{"arxiv_id":"2501.10606","paper":"/paper/differentiable-adversarial-attacks-for-marked","title":"Differentiable Adversarial Attacks for Marked Temporal Point Processes","date":"2025-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"data-iitd/advtpp","path":"Models.py","file_url":"https://github.com/data-iitd/advtpp/blob/HEAD/Models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a5fd5d448fbf7db3","mcp_get_code":{"code_sha256":"a5fd5d448fbf7db3"}},{"arxiv_id":"2406.04229","paper":"/paper/the-clrs-text-algorithmic-reasoning-language","title":"The CLRS-Text Algorithmic Reasoning Language Benchmark","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepmind/clrs","path":"clrs/_src/decoders.py","file_url":"https://github.com/deepmind/clrs/blob/HEAD/clrs/_src/decoders.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b83f762db64e56f0","mcp_get_code":{"code_sha256":"b83f762db64e56f0"}},{"arxiv_id":"2307.00337","paper":"/paper/recursive-algorithmic-reasoning","title":"Recursive Algorithmic Reasoning","date":"2023-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DJayalath/gnn-call-stack","path":"clrs/_src/decoders.py","file_url":"https://github.com/DJayalath/gnn-call-stack/blob/HEAD/clrs/_src/decoders.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b83f762db64e56f0","mcp_get_code":{"code_sha256":"b83f762db64e56f0"}},{"arxiv_id":"2006.12065","paper":"/paper/an-optimal-transport-kernel-for-feature","title":"A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"claying/OTK","path":"otk/layers.py","file_url":"https://github.com/claying/OTK/blob/HEAD/otk/layers.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a5b264a00dc40db","mcp_get_code":{"code_sha256":"6a5b264a00dc40db"}},{"arxiv_id":"2002.01615","paper":"/paper/fast-and-robust-comparison-of-probability","title":"Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces","date":"2020-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joisino/anchor-energy","path":"pytorch/distance_comparison.py","file_url":"https://github.com/joisino/anchor-energy/blob/HEAD/pytorch/distance_comparison.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"99c8f49778a62739","mcp_get_code":{"code_sha256":"99c8f49778a62739"}},{"arxiv_id":"1306.0895","paper":"/paper/sinkhorn-distances-lightspeed-computation-of-1","title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances","date":"2013-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/projected_sinkhorn","path":"projected_sinkhorn/projected_sinkhorn.py","file_url":"https://github.com/locuslab/projected_sinkhorn/blob/HEAD/projected_sinkhorn/projected_sinkhorn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ae919dd4ef56ab8","mcp_get_code":{"code_sha256":"7ae919dd4ef56ab8"}}]}