{"as_of":"2026-08-12T03:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f7104ccd000158a431ff2ee7268b57854d346ec7777a527175ae0ac3eacfc76","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T19:27:03.759298Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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":"2026-06-28T20:44:05.838936Z","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":"2026-06-28T20:52:37.945006Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2501.10116","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.10116","snapshot_observed_at":"2026-06-28T20:52:37.945006Z","title":"Gawm: Global- aware world model for multi-agent reinforcement learning.arXiv preprint arXiv:2501.10116,","venue":null,"work_id":"b9e76e8d-8fe3-4c13-a714-c46b68aac04b","year":null},"citing_paper":{"arxiv_id":"2605.31361","last_updated":"2026-05-29T14:34:50Z","snapshot_observed_at":"2026-08-10T04:03:45.834188Z","submitted_at":"2026-05-29T14:34:50Z","title":"Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T20:44:05.838936Z"},"links":{"cited_paper":"/paper/2501.10116","citing_paper":"/paper/2605.31361"},"observation_digest":"sha256:c3b066722ad6fd737e20ffde100b65e2568dbf5c8b195367c20d485dd90409e7","observation_id":"04a2ca90-ee92-441b-84a5-701aa45f2b52","resolution":{"observed_at":"2026-06-28T20:52:37.946728Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.10116/citation-record","integrity":"/paper/2501.10116/integrity","json":"/paper/2501.10116/citation-record.json","paper":"/paper/2501.10116"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:04.556752Z","title":"Rashid, M","venue":null,"work_id":"89acd23c-8e54-4b43-8343-5541205e4d42","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.589349Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:803302379d31245acec826747ac9e451e61538ea31ef7854bd561eb65744d0b4","observation_id":"1e4f3a84-8192-4087-9e90-4f5d5e0f260b","resolution":{"observed_at":"2026-08-10T19:27:04.561146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.540970Z","title":"Baker, I","venue":null,"work_id":"50877f13-5f93-4d8c-aa5c-18fd2fbc92ca","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.596152Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:f3c0666ad1e658c826cb134813beeff4f3f7e774f93cc7d1497769a44e5d5d83","observation_id":"75b579d8-71e3-42c0-bd62-89b0aa99dafe","resolution":{"observed_at":"2026-08-10T19:27:04.545888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.524473Z","title":null,"venue":null,"work_id":"2183e879-f23f-4ad6-97b4-ecbe9aea5e5d","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.601874Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:209a1fc188fd51955e0be67d12ba8633df07a78c3b2d0c176e3e35be436ece03","observation_id":"e197b4f0-f73e-4f73-810c-fb9102e64a5c","resolution":{"observed_at":"2026-08-10T19:27:04.529534Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.508170Z","title":"Matignon, L","venue":null,"work_id":"10fb02b8-4018-4e11-901f-b3e7a75b35c2","year":2022},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.607273Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:4fa40123d86f0fb97364168f93a4601b91dba1737ffa1730ea0e95ee278910c3","observation_id":"4dc191ed-afd1-44b7-bcda-9014d2c093cc","resolution":{"observed_at":"2026-08-10T19:27:04.513220Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:03.612640Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.612640Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:8d4428de459c455c23196eb15215638b45234b5aea7c490b14d9840c80926041","observation_id":"f660e937-b537-45d9-831d-665c558c385b","resolution":{"observed_at":"2026-08-10T19:27:03.612640Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.12900","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:04.114856Z","title":null,"venue":null,"work_id":"8095aa9a-c204-4072-9a34-c2c6d8838292","year":2025},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.618031Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:d9de83d956236da74ef50c49d507cb1132a86d0644048f764459332edf3d8ae2","observation_id":"9fc5e42b-f433-416e-9c54-ebe6bbd6cd91","resolution":{"observed_at":"2026-08-10T19:27:04.123679Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.robot.2019.01.003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:03.858910Z","title":null,"venue":null,"work_id":"f54559d3-bb6c-4e04-9c93-dbfbc1753fbe","year":2019},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.623678Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:e4a4c167781edf854e1e5312cb5a44e2fad9613b0ea4a91c64095638a2b55db9","observation_id":"b974db50-b5ce-4c01-8981-49075cbaabab","resolution":{"observed_at":"2026-08-10T19:27:03.864446Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.03295","last_updated":"2016-10-11T12:09:03Z","snapshot_observed_at":"2026-08-03T05:49:14.071341Z","submitted_at":"2016-10-11T12:09:03Z","title":"Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.03295","snapshot_observed_at":"2026-08-10T19:27:03.628530Z","title":"Shalev-Shwartz, S","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.628530Z"},"links":{"cited_paper":"/paper/1610.03295","citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:e544c4c10d585dd3a81fa0a5da62141876af9817f69d8c44f492c17e07289a3e","observation_id":"b74e0356-97d4-4e71-8926-be8d4369cf0c","resolution":{"observed_at":"2026-08-10T19:27:03.628530Z","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":"2026-08-10T19:27:03.634068Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.634068Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:0503090352b129afb7b66122b8d8bf6f046170049dfe9b33b55bf15d93f71ba9","observation_id":"3ad4222d-a4ca-49a1-a912-f81b8b45b844","resolution":{"observed_at":"2026-08-10T19:27:03.634068Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-10T19:27:04.485457Z","title":"Hafner, T","venue":null,"work_id":"afaf6412-a7b8-47f4-9341-a4b230fe0e8c","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.639007Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:996eeba061a537d9a2fb32dce515ba64bf6746626e63134f1c2887ad5cdb97e8","observation_id":"c2f9df91-78ca-4835-8a7d-7b889d7db97c","resolution":{"observed_at":"2026-08-10T19:27:04.491052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.469831Z","title":"J ¨anner, J","venue":null,"work_id":"f7bd2098-c4f0-491d-a472-e3a830ce2b7f","year":2019},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.644898Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:f1fc59e0652dbb7291e8ec65c6eb854fdb637d91fa22086a43726251f7d148cf","observation_id":"e27f064c-8082-4918-b8e5-1bbdeaff465d","resolution":{"observed_at":"2026-08-10T19:27:04.474721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.453514Z","title":null,"venue":null,"work_id":"ca02f023-1248-4631-9f9a-7b514c6387c7","year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.650289Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:b95db3b8c74fe93d6823d23d637e060c0f02556bed0b8622a551c22e1214a1e2","observation_id":"4c1e1790-3e45-4938-9643-9410be5bade0","resolution":{"observed_at":"2026-08-10T19:27:04.458356Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neucom.2023.01.076","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:03.841761Z","title":"Malekzadeh, M","venue":null,"work_id":"e1804aa1-62ac-46fe-a160-b26607e9bc29","year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.655396Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:5f0d171a27e6fd4f82de2de7245ae6246a21b79fdd34d1df0b69dababd117a11","observation_id":"cc847927-1452-4c6a-ad87-a8205eb5138f","resolution":{"observed_at":"2026-08-10T19:27:03.846988Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.435517Z","title":"Krupnik, I","venue":null,"work_id":"7f388840-4a3e-4d83-ac6d-2de667b5607a","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.661108Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:a36baf12086d6ba855c26a5624f1151cd0f510e1878ac2cf40255b18742f3473","observation_id":"938ddf40-28c9-4cfd-8c9a-bae8119d9ed4","resolution":{"observed_at":"2026-08-10T19:27:04.441554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.417601Z","title":"Egorov, A","venue":null,"work_id":"a3cc648a-8195-45b8-9225-241762d930e6","year":2022},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.666398Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:3fb9840b07e7cba31b7d1cfe123ccce561d7c3cd91be188cff0b0329f062124b","observation_id":"77090bea-5d87-43cb-9ea6-2256ce5d2c18","resolution":{"observed_at":"2026-08-10T19:27:04.423339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v37i9.26241","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:03.823759Z","title":null,"venue":null,"work_id":"b85bc962-4899-4fe2-9f7c-360001b847bb","year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.672131Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:87e6b3f7e63d0fba31041cf71b3a2696b2135d0ddc8abfb982b7d01f112a1a1b","observation_id":"5641dc86-2952-467f-8dba-928ceab843b4","resolution":{"observed_at":"2026-08-10T19:27:03.829568Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.399504Z","title":"Venugopal, S","venue":null,"work_id":"00eabe42-6d52-455e-b2ea-03369d3281c4","year":2024},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.677114Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:6ec1598944d9c0848067952a29d2225867a3adf549ee7356d25895edc38c8f3f","observation_id":"9c1310c5-a626-4255-901f-53abba85429c","resolution":{"observed_at":"2026-08-10T19:27:04.405243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.379727Z","title":"Samvelyan, T","venue":null,"work_id":"26940da0-5a53-4b68-afdb-220e39dab72c","year":2019},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.683094Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:70012121c44fc61a31f64c38ba5c17f51ca540405e928c208da060512cbe9c4b","observation_id":"45c01360-7d04-49c6-9fab-4e75205b2da9","resolution":{"observed_at":"2026-08-10T19:27:04.385128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.361687Z","title":null,"venue":null,"work_id":"257a1310-19cd-4777-9def-8c11ad95a9c2","year":2016},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.688401Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:42b83c73c54ac89e6f654a8e4224c653c17488755941168c096705f367354b1f","observation_id":"4c8e33f5-7610-4304-8ac8-dc93617d9164","resolution":{"observed_at":"2026-08-10T19:27:04.367560Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.00101","last_updated":"2018-02-28T21:43:37Z","snapshot_observed_at":"2026-08-10T08:40:17.074438Z","submitted_at":"2018-02-28T21:43:37Z","title":"Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.00101","snapshot_observed_at":"2026-08-10T19:27:03.694247Z","title":"Feinberg, A","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.694247Z"},"links":{"cited_paper":"/paper/1803.00101","citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:621356973f50bf0ca656df054e996006809376aacb855dd01c4de568b2e594e6","observation_id":"7f7d327b-294f-42d8-aaa5-deb248478ff8","resolution":{"observed_at":"2026-08-10T19:27:03.694247Z","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":"2026-08-10T19:27:04.342545Z","title":"Ayoub, Z","venue":null,"work_id":"17700976-66e7-4ef2-9515-fc4fea3f7dea","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.700219Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:009911e873878a442983e3430ecbc468bdb2d538a2af38e71b8ef712b6347462","observation_id":"f5029c30-8cb3-43b4-aa18-281070f93641","resolution":{"observed_at":"2026-08-10T19:27:04.348713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.325535Z","title":"Hafner, T","venue":null,"work_id":"b6201cd1-2440-47fb-a635-fe00140d055f","year":2021},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.705455Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:892516a7cd7030f463f6bb025e393d1bef05591387bca4f19f3ffb504f79d7ac","observation_id":"2f5a7af8-3120-44b9-9d2b-a0b1c2fe2d39","resolution":{"observed_at":"2026-08-10T19:27:04.330898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04104","last_updated":"2024-04-17T17:41:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-01-10T18:12:16Z","title":"Mastering Diverse Domains through World Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04104","snapshot_observed_at":"2026-08-10T19:27:03.712587Z","title":"Hafner, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.712587Z"},"links":{"cited_paper":"/paper/2301.04104","citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:ab1c228b202b4ae4a048411cfdfdc84ee9e1b9040c7e41e5e2237fa9f3f5f183","observation_id":"b137cec9-255a-4a95-85c7-ba6d45db026f","resolution":{"observed_at":"2026-08-10T19:27:03.712587Z","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":"2026-08-10T19:27:04.307779Z","title":"Micheli, E","venue":null,"work_id":"b423b619-3408-4476-b881-85201dfb8078","year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.718408Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:4cfcc8f1986ea7a86fdc9973dfdaa0ff59d6fca46df5aaac99546c2429b483f0","observation_id":"ddeca41b-8f39-483a-9ed9-c647bab7eed4","resolution":{"observed_at":"2026-08-10T19:27:04.313469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.287065Z","title":"Zhang, G","venue":null,"work_id":"4bd0b699-0004-4fc5-a447-df03e324b7b9","year":2024},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.723313Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:4716a0ac5de397649c6869f9094dee60953875ad76db5d8bb17e9d4d927aef64","observation_id":"a937f5d2-772c-4ab8-9a52-3e65403564ce","resolution":{"observed_at":"2026-08-10T19:27:04.293434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.268053Z","title":"Robine, M","venue":null,"work_id":"c0085f21-766c-4ac2-8dec-486a1d8351fb","year":2023},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.728425Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:ad5ad3dfbb676fdc7874d86abfde244bbef38518769175c650568f8e721e0d2d","observation_id":"7800396e-938f-4937-b976-355c8a5b2b19","resolution":{"observed_at":"2026-08-10T19:27:04.273313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.250449Z","title":"Vaswani, N","venue":null,"work_id":"124a9916-6ede-4eb7-9e4f-5dcb6ee9c39e","year":2017},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.733570Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:41b199c7aca2d7756719f8107b9dfc2e96a81d879131600ec2918b3a76d11feb","observation_id":"bf9546a4-8a65-4609-a38e-074581a419ed","resolution":{"observed_at":"2026-08-10T19:27:04.255725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:03.739014Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.739014Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:b9e40be7af7219c0917e6ba9ead258fa993bb029d9302cb7cb473aa9387ca605","observation_id":"ab71944f-9036-4a8a-8025-c798117e66ca","resolution":{"observed_at":"2026-08-10T19:27:03.739014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-10T19:27:03.744147Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.744147Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:f404a4c1d55f7edc215eb5bd1f4d9d98bb13112698bf389dd2b7663cf55f882e","observation_id":"54c331a9-e5e1-4d47-9b6c-9860237fb7a7","resolution":{"observed_at":"2026-08-10T19:27:03.744147Z","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":"2026-08-10T19:27:04.233100Z","title":null,"venue":null,"work_id":"7c5d0725-80c7-456f-9990-4d8ce21d06e3","year":2024},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.749516Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:5d4763d17c950a2d88e6781e5334ce6423401ffd0bdc52ae6420c0a5c2428a60","observation_id":"97eeb83b-4e44-4451-bcae-61b878aea8d5","resolution":{"observed_at":"2026-08-10T19:27:04.238340Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T19:27:04.215428Z","title":null,"venue":null,"work_id":"c6c49361-caa2-490e-bda8-1f3f06244d97","year":2022},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.754528Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:da697ebba67c0aa22745e0c9f77a9a7c85d30afc951bef2e25cf7ff498c383aa","observation_id":"637036ff-0eb9-4f37-bd32-6343fdd5e916","resolution":{"observed_at":"2026-08-10T19:27:04.220967Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v34i04.6086","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:03.793248Z","title":null,"venue":null,"work_id":"24e5929f-82fe-4d44-9148-e7eb63411888","year":2020},"citing_paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:03.759298Z"},"links":{"citing_paper":"/paper/2501.10116"},"observation_digest":"sha256:8d7e9b1221ecf0b719ec5332c9a50bf97609dc303e477becb055bd656d2d0493","observation_id":"1c3ff2ea-f7f4-40e5-83f0-550de22b333c","resolution":{"observed_at":"2026-08-10T19:27:03.800337Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.10116","last_updated":"2025-01-17T11:01:56Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-10T19:21:12.774066Z","submitted_at":"2025-01-17T11:01:56Z","title":"GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":4,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":4,"verified_fuzzy":14},"total_outbound_references":32},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2501.10116."}