{"as_of":"2026-08-09T17:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:776621d45e57bd637b0932ad27c876d11f64cc4b1430a1bf61d25e3f0beab2ea","coverage":[{"denominator":10,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:30:45.925848Z","state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T17:33:35.857240Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T23:57:29.172749Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"cited_work":{"arxiv_id":"2507.10843","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.10843","snapshot_observed_at":"2026-07-02T23:57:29.172749Z","title":"arXiv preprint arXiv:2507.10843 , year=","venue":null,"work_id":"ce72b2ea-edfc-4708-9018-8066f0af7a83","year":null},"citing_paper":{"arxiv_id":"2606.09115","last_updated":"2026-06-08T07:11:03Z","snapshot_observed_at":"2026-08-04T22:59:05.251199Z","submitted_at":"2026-06-08T07:11:03Z","title":"Counterfactual Transport Flows for Offline Conservative Trajectory Refinement","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-06-27T17:33:35.857240Z"},"links":{"cited_paper":"/paper/2507.10843","citing_paper":"/paper/2606.09115"},"observation_digest":"sha256:2518bcedc8c192cb650af4afcf4c86f8f2047373f2fe24c2cfa2fbc40cab3118","observation_id":"99fc9320-c981-40d7-8ff0-335790862422","resolution":{"observed_at":"2026-07-02T23:57:29.174231Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.10843/citation-record","integrity":"/paper/2507.10843/integrity","json":"/paper/2507.10843/citation-record.json","paper":"/paper/2507.10843"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:30:46.108661Z","title":null,"venue":null,"work_id":"a10dc6cf-8982-4f99-a123-e6943fc42fa9","year":2022},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.831198Z"},"links":{"citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:e75a12f4360cb49d64fc51fb312a7bba612244d3595f19b34a6682f9d8171ced","observation_id":"1d43cd52-33f8-414b-a1be-40b6a0f85e9e","resolution":{"observed_at":"2026-08-06T17:30:46.185997Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:30:46.036393Z","title":"In contrast, this tendency was not as clearly observed in the HalfCheetah environment","venue":null,"work_id":"41cf39c3-3e49-4774-9d8e-391b90a41aa7","year":2000},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.925848Z"},"links":{"citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:9fd015dc7dae046b588804141bf14dac3a99b9647292cf4bb9147571cc41d9ec","observation_id":"bb140678-6e76-4754-986e-6a3dee57ca80","resolution":{"observed_at":"2026-08-06T17:30:46.073232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01643","last_updated":"2020-11-01T23:50:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-04T17:00:15Z","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.01643","snapshot_observed_at":"2026-08-06T17:30:45.281850Z","title":"Yicheng Luo, zhengyao jiang, Samuel Cohen, Edward Grefenstette, and Marc Peter Deisen- roth","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.281850Z"},"links":{"cited_paper":"/paper/2005.01643","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:a2fd5c66044e1d5aee5688189e036fa2497d018fb6d08235ff68d8aae6c46d46","observation_id":"54148415-905d-4502-887e-efee2d875da4","resolution":{"observed_at":"2026-08-06T17:30:45.281850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.01866","last_updated":"2020-01-09T19:08:09Z","snapshot_observed_at":"2026-08-09T13:50:17.663154Z","submitted_at":"2020-01-07T02:59:59Z","title":"Reinforcement Learning via Fenchel-Rockafellar Duality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.01866","snapshot_observed_at":"2026-08-06T17:30:45.429840Z","title":"Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.429840Z"},"links":{"cited_paper":"/paper/2001.01866","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:986b68b63f6fd2b08034c04be09f95b5ecc27965467f23a02b0d244cf96b2260","observation_id":"98439070-3932-4e90-86ec-5c49b2fdcbb1","resolution":{"observed_at":"2026-08-06T17:30:45.429840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09359","last_updated":"2021-04-24T22:39:30Z","snapshot_observed_at":"2026-07-06T09:29:45.475911Z","submitted_at":"2020-06-16T17:54:41Z","title":"AWAC: Accelerating Online Reinforcement Learning with Offline Datasets","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09359","snapshot_observed_at":"2026-08-06T17:30:45.523687Z","title":"Harshit Sikchi, Qinqing Zheng, Amy Zhang, and Scott Niekum","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.523687Z"},"links":{"cited_paper":"/paper/2006.09359","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:b1d32404d7f3f9131c1739ab03be278c92c60fd77af8fbc63deb16e11e0895bf","observation_id":"84d87a11-543b-492d-aef9-0ee18877939c","resolution":{"observed_at":"2026-08-06T17:30:45.523687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:30:46.496867Z","title":"Wasserstein-2 generative networks","venue":null,"work_id":"ab3a4fe7-9a83-485c-993a-82d6b53fece5","year":2025},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.083649Z"},"links":{"citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:15224604f0a107623bfc20321b3a6577f0daeed3bc36f39467e6345bda2b672e","observation_id":"ff0ed2f6-bbf0-455a-b574-9aa139060114","resolution":{"observed_at":"2026-08-06T17:30:46.562888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11361","last_updated":"2019-11-26T06:11:34Z","snapshot_observed_at":"2026-07-06T08:39:58.361914Z","submitted_at":"2019-11-26T06:11:34Z","title":"Behavior Regularized Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11361","snapshot_observed_at":"2026-08-06T17:30:45.689569Z","title":"Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xi- anyuan Zhan","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.689569Z"},"links":{"cited_paper":"/paper/1911.11361","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:7502a0eaeb13e50f181f07f39af9e1187c10b87d350d7839f0d0979cd8b3e427","observation_id":"71468324-58d0-456d-979f-a06f0f009812","resolution":{"observed_at":"2026-08-06T17:30:45.689569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07219","last_updated":"2021-02-06T01:57:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-04-15T17:18:19Z","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.07219","snapshot_observed_at":"2026-08-06T17:30:44.978852Z","title":"Scott Fujimoto and Shixiang Gu","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:44.978852Z"},"links":{"cited_paper":"/paper/2004.07219","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:a79dfa4f717ed4b11cd1b7dc41ee3f5a2b7d3151dacf208a01b875e79298e394","observation_id":"4c3fafe3-c5c6-4f2e-b1d2-cbf8ddad0e00","resolution":{"observed_at":"2026-08-06T17:30:44.978852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12324","last_updated":"2022-01-28T18:43:41Z","snapshot_observed_at":"2026-08-09T00:29:12.885708Z","submitted_at":"2022-01-28T18:43:41Z","title":"Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12324","snapshot_observed_at":"2026-08-06T17:30:44.825946Z","title":"Optimal transport tools (ott): A jax toolbox for all things wasserstein","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:44.825946Z"},"links":{"cited_paper":"/paper/2201.12324","citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:b6373a543e85ac3bf7c0e4cc62db0a0349954d1ef811d171fa7a58cc3d1acab1","observation_id":"57c7f0c7-d0dc-4cd0-b240-22ed0c0e1208","resolution":{"observed_at":"2026-08-06T17:30:44.825946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:30:46.307747Z","title":"7 Experimental Details In AdvW and Q-DOT, the actor, critic, discriminator (for AdvW), and ICNN (for Q-DOT) are all two-layer MLPs with ReLU activations and 256 hidden units","venue":null,"work_id":"3c49c845-b970-45ab-92ee-3b15e91612e0","year":2019},"citing_paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:45.751630Z"},"links":{"citing_paper":"/paper/2507.10843"},"observation_digest":"sha256:ad8ff8654cb75c4e62c5e69d76a88dee296046952f7d6d8cdc0534728d44a2f8","observation_id":"bcb3c231-dd35-46a4-8d32-67ca568d2537","resolution":{"observed_at":"2026-08-06T17:30:46.407669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.10843","last_updated":"2025-07-14T22:28:36Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T00:29:43.167979Z","submitted_at":"2025-07-14T22:28:36Z","title":"Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps"},"reference_resolution":{"displayed":10,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":3},"total_outbound_references":10},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 1 inbound Pith citation observation for arXiv:2507.10843."}