{"as_of":"2026-08-13T19:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:51d062ce5a48f976b8a43ee4afd12ea8a5a452d6da65f58e51051bced8023750","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:04:02.191960Z","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":"2026-05-24T03:28:50.279855Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2010.04740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph convolutional value decomposi- tion in multi-agent reinforcement learning","venue":null,"work_id":"8221900d-6855-449a-a23b-224fccebf2ba","year":2010},"citing_paper":{"arxiv_id":"2403.19253","last_updated":"2026-04-10T00:20:59Z","snapshot_observed_at":"2026-07-06T17:52:26.380307Z","submitted_at":"2024-03-28T09:20:15Z","title":"Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T03:26:12.175794Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2403.19253"},"observation_digest":"sha256:f205c518c151872c7203c2f3a1992606dcaad0ba890f9bc5a19a1f3a765ffb6a","observation_id":"05b20a7e-53c4-4ce0-a559-d5f20bfea7a0","resolution":{"observed_at":"2026-05-24T03:28:50.283141Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-08-10T23:04:02.191960Z","title":"Graph convolutional value decomposition in multi-agent reinforcement learning","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.00165","last_updated":"2024-12-30T22:16:50Z","snapshot_observed_at":"2026-08-11T01:57:51.381842Z","submitted_at":"2024-12-30T22:16:50Z","title":"Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T23:04:02.191960Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2501.00165"},"observation_digest":"sha256:b9f01cf8e89c42dd432a32592df8bd5957d98e242bd09c1130d49bd8f245f311","observation_id":"0c82be9f-b531-4a46-9f6e-c64e09b90afc","resolution":{"observed_at":"2026-08-10T23:04:02.191960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-08-09T10:52:39.141965Z","title":"Graph convo- lutional value decomposition in multi-agent reinforcement learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02875","last_updated":"2025-02-05T04:30:06Z","snapshot_observed_at":"2026-08-13T15:10:33.087555Z","submitted_at":"2025-02-05T04:30:06Z","title":"Heterogeneous Value Decomposition Policy Fusion for Multi-Agent Cooperation","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-09T10:52:39.141965Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2502.02875"},"observation_digest":"sha256:e43e650a7fed20856963c7c84fabbceee7918d3b6aac826417cefd6e4cf0ec59","observation_id":"f3fd1ea1-0396-4a11-aa80-ce8eeaa8fc7a","resolution":{"observed_at":"2026-08-09T10:52:39.141965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-08-06T10:40:38.829339Z","title":"Graph convolutional value decomposition in multi-agent reinforcement learning.arXiv preprint arXiv:2010.04740, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.23604","last_updated":"2026-06-30T11:26:47Z","snapshot_observed_at":"2026-08-13T17:55:52.967098Z","submitted_at":"2025-07-31T14:42:12Z","title":"Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T10:40:38.829339Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2507.23604"},"observation_digest":"sha256:ab1fe2c9fe07b0e1adad127f338f4d645ec37f8d31aa1ecc9fd9f515f15c2680","observation_id":"ee481667-cc6f-492c-95f2-e1d92a6b28db","resolution":{"observed_at":"2026-08-06T10:40:38.829339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2010.04740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph convolutional value decomposi- tion in multi-agent reinforcement learning","venue":null,"work_id":"8221900d-6855-449a-a23b-224fccebf2ba","year":2010},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-12T01:31:19.576223Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:ea2a1829f86e138ea505ab500406183041050bdccb5b3a693474100e3f972d46","observation_id":"e34d698f-cd12-41ac-930b-58121c360242","resolution":{"observed_at":"2026-05-12T07:56:27.351332Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2010.04740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04740","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph convolutional value decomposi- tion in multi-agent reinforcement learning","venue":null,"work_id":"8221900d-6855-449a-a23b-224fccebf2ba","year":2010},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-20T22:34:46.440511Z"},"links":{"cited_paper":"/paper/2010.04740","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:802216763270876b3f83f235b734dea1ac1cc5a173cc749d8ae8db55b63ddad7","observation_id":"32b898a1-739d-4b66-9e1c-404ec7b42833","resolution":{"observed_at":"2026-05-20T22:39:10.742765Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2010.04740/citation-record","integrity":"/paper/2010.04740/integrity","json":"/paper/2010.04740/citation-record.json","paper":"/paper/2010.04740"},"outbound":[],"paper":{"arxiv_id":"2010.04740","last_updated":"2021-02-10T07:33:31Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T16:36:35.235872Z","submitted_at":"2020-10-09T18:01:01Z","title":"Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2010.04740."}