{"as_of":"2026-08-07T22:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:708cbc362b5bb7a78ae0cc65704d005dd72738abb50dd88045a9668ffa9a32dc","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:16:03.533280Z","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":"2026-08-06T20:39:09.115942Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.20523","last_updated":"2024-12-29T17:15:40Z","snapshot_observed_at":"2026-07-06T20:14:22.094082Z","submitted_at":"2024-12-29T17:15:40Z","title":"Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.20523","snapshot_observed_at":"2026-08-07T04:16:03.533280Z","title":"Game Theory and Multi -Agent Reinforcement Learning: From Nash Equilibria to Evolutionary Dynamics","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11304","last_updated":"2025-06-12T21:09:27Z","snapshot_observed_at":"2026-08-07T04:09:16.859961Z","submitted_at":"2025-06-12T21:09:27Z","title":"A Hybrid Adaptive Nash Equilibrium Solver for Distributed Multi-Agent Systems with Game-Theoretic Jump Triggering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:16:03.533280Z"},"links":{"cited_paper":"/paper/2412.20523","citing_paper":"/paper/2506.11304"},"observation_digest":"sha256:5d6cb991f3a3f5524ea80172d3fd7b179338d771e578b081da1de4d2ad8a49b9","observation_id":"faeda498-a040-4cf2-9d47-07774696dc45","resolution":{"observed_at":"2026-08-07T04:16:03.533280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20523","last_updated":"2024-12-29T17:15:40Z","snapshot_observed_at":"2026-07-06T20:14:22.094082Z","submitted_at":"2024-12-29T17:15:40Z","title":"Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics","version":1},"cited_work":{"arxiv_id":"2412.20523","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.20523","snapshot_observed_at":"2026-08-06T20:39:09.115942Z","title":"Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics","venue":"cs.MA","work_id":"7167b025-0b79-4813-b9a2-805b0e494546","year":2024},"citing_paper":{"arxiv_id":"2507.02211","last_updated":"2025-07-04T13:32:01Z","snapshot_observed_at":"2026-08-06T20:32:14.101256Z","submitted_at":"2025-07-03T00:17:53Z","title":"Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T20:39:08.748931Z"},"links":{"cited_paper":"/paper/2412.20523","citing_paper":"/paper/2507.02211"},"observation_digest":"sha256:bcdf3a47b37ac9dab2b833c5dd501a06fb840aba364e2373711417acfa7e6b41","observation_id":"ecf4e5e5-79fe-4f14-8454-20926d83978f","resolution":{"observed_at":"2026-08-06T20:39:09.227460Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.20523/citation-record","integrity":"/paper/2412.20523/integrity","json":"/paper/2412.20523/citation-record.json","paper":"/paper/2412.20523"},"outbound":[],"paper":{"arxiv_id":"2412.20523","last_updated":"2024-12-29T17:15:40Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-07-06T20:14:22.094082Z","submitted_at":"2024-12-29T17:15:40Z","title":"Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.20523."}