{"as_of":"2026-08-11T07:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ef0b5a9b1d54ea08cb8f405c583f339cbb1d3d3fcc35705a4731e3bc455461cc","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T23:14:16.301547Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-03T07:57:44.574102Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1506.01170","last_updated":"2015-06-03T09:12:58Z","snapshot_observed_at":"2026-07-06T04:19:46.347735Z","submitted_at":"2015-06-03T09:12:58Z","title":"A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.01170","snapshot_observed_at":"2026-08-07T23:14:16.301547Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.08950","last_updated":"2025-02-13T04:17:43Z","snapshot_observed_at":"2026-08-09T23:58:10.785620Z","submitted_at":"2025-02-13T04:17:43Z","title":"Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T23:14:16.301547Z"},"links":{"cited_paper":"/paper/1506.01170","citing_paper":"/paper/2502.08950"},"observation_digest":"sha256:a5c6d1304c7566d5e9c7ee2730772f7b1e01768796b59b68010901dc44e3a8b6","observation_id":"277fe8ca-e74c-4191-b180-1f186fb1057b","resolution":{"observed_at":"2026-08-07T23:14:16.301547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.01170","last_updated":"2015-06-03T09:12:58Z","snapshot_observed_at":"2026-07-06T04:19:46.347735Z","submitted_at":"2015-06-03T09:12:58Z","title":"A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems","version":1},"cited_work":{"arxiv_id":"1506.01170","doi":null,"metadata_source":"pith","pith_arxiv_id":"1506.01170","snapshot_observed_at":"2026-07-03T07:57:44.574102Z","title":"A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems","venue":"cs.GT","work_id":"22f2002d-3242-439e-b8cd-ade408bcd58c","year":2015},"citing_paper":{"arxiv_id":"2604.08728","last_updated":"2026-04-09T19:42:17Z","snapshot_observed_at":"2026-07-06T22:57:45.084987Z","submitted_at":"2026-04-09T19:42:17Z","title":"Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T17:01:35.037646Z"},"links":{"cited_paper":"/paper/1506.01170","citing_paper":"/paper/2604.08728"},"observation_digest":"sha256:a4cdeab252af67b16b1838a6b73a74283698b17979bc4e5834f9553fa881b31d","observation_id":"87da9f9c-fa1e-4642-aacc-9de5109cebad","resolution":{"observed_at":"2026-05-11T07:41:01.325374Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"1506.01170","last_updated":"2015-06-03T09:12:58Z","snapshot_observed_at":"2026-07-06T04:19:46.347735Z","submitted_at":"2015-06-03T09:12:58Z","title":"A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems","version":1},"cited_work":{"arxiv_id":"1506.01170","doi":null,"metadata_source":"pith","pith_arxiv_id":"1506.01170","snapshot_observed_at":"2026-07-03T07:57:44.574102Z","title":"A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems","venue":"cs.GT","work_id":"22f2002d-3242-439e-b8cd-ade408bcd58c","year":2015},"citing_paper":{"arxiv_id":"2606.10261","last_updated":"2026-06-09T00:03:27Z","snapshot_observed_at":"2026-07-06T23:49:27.565372Z","submitted_at":"2026-06-09T00:03:27Z","title":"Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners","version":1},"reference_index":182,"source":"arxiv_source","source_observed_at":"2026-06-27T11:35:24.994137Z"},"links":{"cited_paper":"/paper/1506.01170","citing_paper":"/paper/2606.10261"},"observation_digest":"sha256:b7782af6e35a47395d7716894081a8a13ef902cd7aff34c1d137849f7747a431","observation_id":"0800cf67-6e8f-4516-b0d4-e6d09c763380","resolution":{"observed_at":"2026-07-03T07:57:44.576987Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/1506.01170/citation-record","integrity":"/paper/1506.01170/integrity","json":"/paper/1506.01170/citation-record.json","paper":"/paper/1506.01170"},"outbound":[],"paper":{"arxiv_id":"1506.01170","last_updated":"2015-06-03T09:12:58Z","latest_version":1,"primary_category":"cs.GT","snapshot_observed_at":"2026-07-06T04:19:46.347735Z","submitted_at":"2015-06-03T09:12:58Z","title":"A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1506.01170."}