{"as_of":"2026-08-14T19:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4e40e20e23c8ee5745732d1f692e57c192829cdaa06051e3cf9a08d372558e3a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":16,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:42:28.199222Z","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":"2026-07-04T04:19:34.389419Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-14T14:42:28.199222Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"1908.02805","last_updated":"2021-11-04T18:24:45Z","snapshot_observed_at":"2026-08-14T14:31:27.179318Z","submitted_at":"2019-08-07T19:25:37Z","title":"Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T14:42:28.199222Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/1908.02805"},"observation_digest":"sha256:9bc33d81f038f631e8001041fb78c1fa4abdfb80e09fc86b6cdebad578b0c11d","observation_id":"6eb6318d-1818-47b9-8b8a-758f95e86eb9","resolution":{"observed_at":"2026-08-14T14:42:28.199222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-14T13:59:09.714113Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"1908.03963","last_updated":"2021-04-30T04:14:28Z","snapshot_observed_at":"2026-08-14T14:14:37.598126Z","submitted_at":"2019-08-11T21:40:11Z","title":"A Review of Cooperative Multi-Agent Deep Reinforcement Learning","version":4},"reference_index":221,"source":"arxiv_source","source_observed_at":"2026-08-14T13:59:09.714113Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/1908.03963"},"observation_digest":"sha256:8f15eaf4764b04ca2f22f63741e21806b9df1bbe728c608915811257979c2889","observation_id":"517df901-7ab0-48f5-88e5-606244310063","resolution":{"observed_at":"2026-08-14T13:59:09.714113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-11T18:46:17.577423Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.07639","last_updated":"2024-12-18T09:04:32Z","snapshot_observed_at":"2026-08-14T15:46:26.284752Z","submitted_at":"2024-12-10T16:19:08Z","title":"Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T18:46:17.577423Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2412.07639"},"observation_digest":"sha256:0217f897f31fdee4af83735f38f0086c050cdf4f6e7c4e4f68dfc3c493fc8d2f","observation_id":"b104da63-e063-4565-8a43-fbb44c8287df","resolution":{"observed_at":"2026-08-11T18:46:17.577423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-09T04:16:44.683629Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04388","last_updated":"2025-07-01T14:33:24Z","snapshot_observed_at":"2026-08-14T14:00:40.361376Z","submitted_at":"2025-02-05T22:20:15Z","title":"Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T04:16:44.683629Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2502.04388"},"observation_digest":"sha256:d73a8439364b16fb0b3fa5545b430f095f365263701a9fa9b1e707d18e59a67a","observation_id":"e4a9a22d-6d13-465f-9a49-564d6681719c","resolution":{"observed_at":"2026-08-09T04:16:44.683629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-07T14:37:49.906712Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.18397","last_updated":"2025-08-24T02:49:58Z","snapshot_observed_at":"2026-08-07T15:03:15.624729Z","submitted_at":"2025-05-23T22:05:19Z","title":"An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:37:49.906712Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2505.18397"},"observation_digest":"sha256:6bd8d067e4b7083b6913b6b0f90b4e78401487a6529e817e3fe6658a77e81d7e","observation_id":"e648a3ec-1229-4580-afbe-43ba7f807f37","resolution":{"observed_at":"2026-08-07T14:37:49.906712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-07T12:38:15.814299Z","title":"Zhang, Z","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24198","last_updated":"2025-06-03T22:45:46Z","snapshot_observed_at":"2026-08-14T07:20:35.381448Z","submitted_at":"2025-05-30T04:18:09Z","title":"Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:15.814299Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2505.24198"},"observation_digest":"sha256:11123c6b1f5d7fd744b39bc7dd90cea0a5fc27f19825d65c57a9daa82a95085a","observation_id":"dc99008a-bbf6-47f9-bb6c-dd8c29471662","resolution":{"observed_at":"2026-08-07T12:38:15.814299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-05T23:01:13.122644Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2508.06033","last_updated":"2025-08-08T05:38:17Z","snapshot_observed_at":"2026-08-11T21:55:01.957145Z","submitted_at":"2025-08-08T05:38:17Z","title":"InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T23:01:13.122644Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2508.06033"},"observation_digest":"sha256:4f5fb3a8bc1b3655df2868b8e71498361bf478f21f7b4fb66faabd896780d70e","observation_id":"4e7552fe-9ec1-4779-9add-e152652ee752","resolution":{"observed_at":"2026-08-05T23:01:13.122644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-05T23:04:42.495007Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2508.06042","last_updated":"2025-08-08T05:57:12Z","snapshot_observed_at":"2026-08-13T14:57:49.720698Z","submitted_at":"2025-08-08T05:57:12Z","title":"Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-05T23:04:42.495007Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2508.06042"},"observation_digest":"sha256:f4f02a9ed4cba585c72ef6d4e7440d861e02db7fba4339bd4ec51baaecc9b947","observation_id":"ed5db4b2-7bf0-4c88-844a-4bcb4289c5f3","resolution":{"observed_at":"2026-08-05T23:04:42.495007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-04T11:42:03.984192Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2510.03699","last_updated":"2025-10-04T06:40:32Z","snapshot_observed_at":"2026-08-08T23:55:17.091848Z","submitted_at":"2025-10-04T06:40:32Z","title":"Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T11:42:03.984192Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2510.03699"},"observation_digest":"sha256:ce7a649756291c10dc2978d46a3ef6e3ca9fc8d41b040e95e4f2b5ba784ed888","observation_id":"db43a0be-af36-4926-af6c-6f45fb180eb1","resolution":{"observed_at":"2026-08-04T11:42:03.984192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2604.08303","last_updated":"2026-05-02T15:28:24Z","snapshot_observed_at":"2026-07-06T22:57:26.678728Z","submitted_at":"2026-04-09T14:35:44Z","title":"Stability and Sensitivity Analysis for Objective Misspecifications Among Model Predictive Game Controllers","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T17:33:09.731253Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2604.08303"},"observation_digest":"sha256:37ad26ee7c166f569e84d810650dd030bdf005e64fcef0d105a255964793137b","observation_id":"e64f6ae8-c530-42ed-af8f-c119fab24c5a","resolution":{"observed_at":"2026-05-11T06:40:58.618306Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2604.17240","last_updated":"2026-04-19T04:02:17Z","snapshot_observed_at":"2026-08-13T05:08:05.094751Z","submitted_at":"2026-04-19T04:02:17Z","title":"Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T06:42:57.319962Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2604.17240"},"observation_digest":"sha256:b05f89d4fd861da9b10f8bb403383566dadaf39e8f398e7d11870a7257f61d0a","observation_id":"87cc5a13-3c72-480a-8ccb-cb0f7985bf53","resolution":{"observed_at":"2026-05-10T06:46:37.336629Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2605.03569","last_updated":"2026-05-05T09:40:30Z","snapshot_observed_at":"2026-08-13T06:11:53.783316Z","submitted_at":"2026-05-05T09:40:30Z","title":"Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-07T13:30:52.951561Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2605.03569"},"observation_digest":"sha256:33f930f3f0caec13d5447a87787d0109218aa2f61916f7850d42c08044148e68","observation_id":"ecf8cea1-68d7-40f8-9922-bd515730c543","resolution":{"observed_at":"2026-05-12T11:01:30.551804Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2605.14235","last_updated":"2026-05-14T01:03:41Z","snapshot_observed_at":"2026-08-14T05:15:59.117715Z","submitted_at":"2026-05-14T01:03:41Z","title":"Quantum Advantage in Multi Agent Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T01:47:50.831972Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2605.14235"},"observation_digest":"sha256:041c6f3267e3f6e7a5fba63d71a9e806b0d110b39f8d5de9fabd6c6b250fee09","observation_id":"bdc457d2-6957-49a8-a7e6-136ab003b6ca","resolution":{"observed_at":"2026-05-15T01:48:28.085323Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2606.19920","last_updated":"2026-06-18T08:14:43Z","snapshot_observed_at":"2026-08-13T22:04:27.625451Z","submitted_at":"2026-06-18T08:14:43Z","title":"Deep-Unfolded Coordination","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T17:05:44.372648Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2606.19920"},"observation_digest":"sha256:6a98bd2f28d88c478af1fe13b6cff5ba8322d75df42f2357ae100df722a67883","observation_id":"77dc2281-2e8d-4a2b-be15-f7b0750dd93b","resolution":{"observed_at":"2026-07-04T04:19:34.391301Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":"1911.10635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-07-04T04:19:34.389419Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021","venue":null,"work_id":"b1838b5b-ea25-4179-9ea4-359a86717a87","year":1911},"citing_paper":{"arxiv_id":"2606.20700","last_updated":"2026-06-15T10:36:10Z","snapshot_observed_at":"2026-08-13T01:54:26.790692Z","submitted_at":"2026-06-15T10:36:10Z","title":"Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T02:30:22.787158Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2606.20700"},"observation_digest":"sha256:7d4ee89795cd4098c373ab91fd2e8d163b1ffd0ea65dbc131867a36f17207a1c","observation_id":"736fa159-84d0-474f-8589-f2b3cd0688ca","resolution":{"observed_at":"2026-07-03T18:38:49.901733Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10635","snapshot_observed_at":"2026-08-10T04:59:12.962786Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2608.07418","last_updated":"2026-08-07T17:04:41Z","snapshot_observed_at":"2026-08-14T17:43:10.274749Z","submitted_at":"2026-08-07T17:04:41Z","title":"ResidencyRL: Reinforcement Learning in Simulated Clinical Environments","version":1},"reference_index":192,"source":"arxiv_source","source_observed_at":"2026-08-10T04:59:12.962786Z"},"links":{"cited_paper":"/paper/1911.10635","citing_paper":"/paper/2608.07418"},"observation_digest":"sha256:2dbb8077c390662f01fd2c9e672e9b930f75be66118bed128ecca14b6b770edf","observation_id":"4030d70c-3abb-4b8a-b322-5ecb71e54a5d","resolution":{"observed_at":"2026-08-10T04:59:12.962786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1911.10635/citation-record","integrity":"/paper/1911.10635/integrity","json":"/paper/1911.10635/citation-record.json","paper":"/paper/1911.10635"},"outbound":[],"paper":{"arxiv_id":"1911.10635","last_updated":"2021-04-28T21:33:13Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T14:49:36.216169Z","submitted_at":"2019-11-24T22:50:32Z","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:1911.10635."}