{"as_of":"2026-08-17T16:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03728353e5b2801864e9c4c1a5389b8a6a79395ed9cce4e8d09f2a51834ba266","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:54:47.414689Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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":"2026-05-21T23:08:08.096620Z","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":"2026-05-21T23:10:44.714270Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2506.12497","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.12497","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Wasserstein-barycenter consensus for cooperative multi- agent reinforcement learning","venue":null,"work_id":"cef5c770-5ec0-4dc5-a96e-b5eda4dd38e9","year":2025},"citing_paper":{"arxiv_id":"2509.14521","last_updated":"2026-05-18T12:21:35Z","snapshot_observed_at":"2026-08-15T04:09:34.787128Z","submitted_at":"2025-09-18T01:28:03Z","title":"Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-21T23:08:08.096620Z"},"links":{"cited_paper":"/paper/2506.12497","citing_paper":"/paper/2509.14521"},"observation_digest":"sha256:1d998dd430470d36a45df4a2e61b48610ca24de7571b12a28fcb21fbc76b0363","observation_id":"3a27ee60-33fd-4334-9fc9-ba49c7b988e1","resolution":{"observed_at":"2026-05-21T23:10:44.715867Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.12497/citation-record","integrity":"/paper/2506.12497/integrity","json":"/paper/2506.12497/citation-record.json","paper":"/paper/2506.12497"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.12055","last_updated":"2023-10-18T15:42:53Z","snapshot_observed_at":"2026-08-17T09:45:45.530534Z","submitted_at":"2023-10-18T15:42:53Z","title":"Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12055","snapshot_observed_at":"2026-08-07T00:54:46.227463Z","title":"Understanding reward ambiguity through optimal transport theory in inverse reinforcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.227463Z"},"links":{"cited_paper":"/paper/2310.12055","citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:5d02a23d58542ddf850e942d25b0d8d16132f67529f69f9c39665b50b8a643c8","observation_id":"2f42733e-c511-4ad0-b335-1fc0f1732b30","resolution":{"observed_at":"2026-08-07T00:54:46.227463Z","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":"2026-08-07T00:54:48.885547Z","title":"W AVE : Wasserstein adaptive value estimation for actor-critic reinforcement learning","venue":null,"work_id":"03e00d0e-7f23-489a-a6a6-1683739ff81e","year":2025},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.303929Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:9a3a28921f437fb884ea0f7c2bf3bc9005d557a05aebab7abd8964e675f1cfbd","observation_id":"84e1b523-0713-4f3b-9761-1a340b31ae29","resolution":{"observed_at":"2026-08-07T00:54:48.990993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:48.598584Z","title":"A comprehensive survey of multiagent reinforcement learning","venue":null,"work_id":"ae45f284-7e38-4638-96bf-fb94770eea16","year":2008},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.371448Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:4c9c74e73a7f550d1c8936c91a420fae5bcca895be546b7e57b78b94d1beaa21","observation_id":"6d058de6-474e-4b44-9f7a-4bb24a8eb3c4","resolution":{"observed_at":"2026-08-07T00:54:48.730019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:54:46.460203Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.460203Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:7e12384c5bdedf1bb9aaba8cd982601861afedf3d49e2d7d491f9892fe5f65b2","observation_id":"c726fe8f-7fe6-478c-b72d-62fe210d7b63","resolution":{"observed_at":"2026-08-07T00:54:46.460203Z","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":"2026-08-07T00:54:48.456865Z","title":"Counterfactual multi-agent policy gradients","venue":null,"work_id":"668583d7-5c55-48eb-9ef7-ee38861d82a9","year":2018},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.524696Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:479ec0f1977bda2825988e94687793cecbdc7c818c2c978f0000c8ffc915c092","observation_id":"c59024bb-4219-48ad-9934-fde4d456da8c","resolution":{"observed_at":"2026-08-07T00:54:48.465082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:48.371152Z","title":"Learning generative models with sinkhorn divergences","venue":null,"work_id":"1292983b-7a4a-4b59-ad16-3ecf79ec6e31","year":2018},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.620409Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:0ad2520673693a1cba03bf66edfed54b970ab469e4fba78f48a98227a6d52ec5","observation_id":"f8d904c0-40f6-4a96-9182-aedfcc5ebaff","resolution":{"observed_at":"2026-08-07T00:54:48.449756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:48.234627Z","title":"On centralized critics in multi-agent reinforcement learning","venue":null,"work_id":"6f10319c-5021-4405-883c-faf014155ef2","year":2023},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.724690Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:487bec50019fa03bcb8b09dd7cd20ce99ad7afd4cda94afa360a694536d42e2d","observation_id":"372ee92a-97fe-4dd6-ac43-2e00abc7bb80","resolution":{"observed_at":"2026-08-07T00:54:48.303839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:48.074914Z","title":"Offline reinforcement learning with wasserstein regularization via optimal transport maps","venue":null,"work_id":"f7538b05-e109-4b47-a4d2-d147175d6e40","year":null},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.791405Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:77033e52b12d5fd2f18d50a0ce6bf60ae4e0f9630d6a6cc5102dc6d7a7ccbfca","observation_id":"34068ae7-78f9-436f-af10-7ffc26f869c4","resolution":{"observed_at":"2026-08-07T00:54:48.140398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:54:46.851586Z","title":"Computational optimal transport: With applications to data science","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.851586Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:3070a67fc3355b19f1215fd27a726219ef914473862770798e2031ed676025ec","observation_id":"886e0ab9-092a-4922-affa-0fb8e0837136","resolution":{"observed_at":"2026-08-07T00:54:46.851586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.16328","last_updated":"2025-06-14T12:48:51Z","snapshot_observed_at":"2026-08-16T12:56:04.089274Z","submitted_at":"2025-02-22T19:14:36Z","title":"Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2502.16328","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.16328","snapshot_observed_at":"2026-08-07T00:54:47.620716Z","title":"Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning","venue":"cs.LG","work_id":"516c4d73-06e5-4db6-8588-740cdb0e2ad7","year":2025},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:46.931228Z"},"links":{"cited_paper":"/paper/2502.16328","citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:b2ec944e9554f9facc8813efc51343d2f2a3c61024b39f35eaa71215bc00ab9e","observation_id":"d782aa7f-4058-4561-9759-a1608ae3790c","resolution":{"observed_at":"2026-08-07T00:54:47.660350Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:47.947836Z","title":"and Lu, Z","venue":null,"work_id":"2a3ec54a-cbed-477e-9b56-159313c34247","year":2022},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.022783Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:ec94660b4daac42aaec14d95237da23e423ca75c21b1eaf90fad89aca87f4f4a","observation_id":"fc2fb200-27fd-4ffe-94f1-7fb041096eb9","resolution":{"observed_at":"2026-08-07T00:54:47.994308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.13625","last_updated":"2023-10-31T05:06:10Z","snapshot_observed_at":"2026-08-10T13:45:07.842716Z","submitted_at":"2020-05-27T20:14:28Z","title":"Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.13625","snapshot_observed_at":"2026-08-07T00:54:47.107571Z","title":"K., Grammel, N., Son, S., Black, B., and Agrawal, A","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.107571Z"},"links":{"cited_paper":"/paper/2005.13625","citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:d44aa8257ac090dcd98ea9133ba644ed4111f8472595fe8887e975365efadffc","observation_id":"0db4bfa3-e463-4b1a-8aea-75324d59aa9b","resolution":{"observed_at":"2026-08-07T00:54:47.107571Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:54:47.159121Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.159121Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:5a1f4b94dc13c5b7ed5ccdbb841549d2bc44eda01732bd6a861daadc0d09cdca","observation_id":"b5d0eea6-5c5e-4e18-a68f-8a40f1038674","resolution":{"observed_at":"2026-08-07T00:54:47.159121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03172","last_updated":"2024-03-05T18:07:34Z","snapshot_observed_at":"2026-08-16T14:12:33.477766Z","submitted_at":"2024-03-05T18:07:34Z","title":"Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination","version":1},"cited_work":{"arxiv_id":"2403.03172","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.03172","snapshot_observed_at":"2026-08-07T00:54:47.495036Z","title":"Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination","venue":"cs.AI","work_id":"44102462-9e50-4249-8476-dae1ba956eca","year":2024},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.211544Z"},"links":{"cited_paper":"/paper/2403.03172","citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:7aa2fd840c9cf33e89cc6ac917807af97a83d9d286450a7b2274043c8417da01","observation_id":"c4695893-ba4d-4cab-95bb-3bbc59ca3369","resolution":{"observed_at":"2026-08-07T00:54:47.545452Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-07T00:54:47.831569Z","title":"Learning to share in networked multi-agent reinforcement learning","venue":null,"work_id":"acf37719-6841-434d-9dd2-527a75de4db3","year":2022},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.253064Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:3b5a28a746ca0afdb5a0a3faa6a08fbc324e64d9c9ef00b1d024ee7a490b32af","observation_id":"cacbca4f-d833-4410-9e41-b02012472d8a","resolution":{"observed_at":"2026-08-07T00:54:47.884663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.01058","last_updated":"2023-12-02T08:04:31Z","snapshot_observed_at":"2026-08-16T14:38:16.715731Z","submitted_at":"2023-12-02T08:04:31Z","title":"A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.01058","snapshot_observed_at":"2026-08-07T00:54:47.313334Z","title":"A survey of progress on cooperative multi-agent reinforcement learning in open environment","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.313334Z"},"links":{"cited_paper":"/paper/2312.01058","citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:3bc5931a18e474c5b6620ce636c934dc00f9598f56d940592f19177df220a698","observation_id":"87965751-f283-4f5b-90f9-3e2e537673df","resolution":{"observed_at":"2026-08-07T00:54:47.313334Z","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":"2026-08-07T00:54:47.735297Z","title":"A semi-independent policies training method with shared representation for heterogeneous multi-agents reinforcement learning","venue":null,"work_id":"12154980-242f-4e8b-86b0-e508e84ff322","year":2023},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.365347Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:b45d2df1eb81d07b98b74086221e95270bb6c1cb0fbaf7a737945eef8b2f2531","observation_id":"292daf96-c009-42a2-a788-c17c0a617d7f","resolution":{"observed_at":"2026-08-07T00:54:47.777866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:54:47.414689Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T00:54:47.414689Z"},"links":{"citing_paper":"/paper/2506.12497"},"observation_digest":"sha256:a247b494bfb02f2bfd5b8e8d2fc355339b8ee977ec1ad3f4acc0771022197566","observation_id":"7e81fb14-5203-486e-8544-d2e733049abe","resolution":{"observed_at":"2026-08-07T00:54:47.414689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.12497","last_updated":"2025-06-18T14:00:38Z","latest_version":2,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-09T19:59:48.246401Z","submitted_at":"2025-06-14T13:17:47Z","title":"Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":9},"total_outbound_references":18},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2506.12497."}