{"as_of":"2026-08-16T14:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2fc394a3fdcd05dc0dabbf77081d09c99364d654fb5a1faf61189c1d1f62601f","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":"2026-08-16T06:30:59.297886+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":"2026-08-05T14:49:53.209213Z","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":"2026-05-23T06:15:27.555035Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2204.12190","last_updated":"2022-04-26T09:48:28Z","snapshot_observed_at":"2026-08-16T00:40:11.413099Z","submitted_at":"2022-04-26T09:48:28Z","title":"Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method","version":1},"cited_work":{"arxiv_id":"2204.12190","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2204.12190","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"dabf29bd-48b4-4b8a-b4ec-a7a435fe2ec8","year":2022},"citing_paper":{"arxiv_id":"2501.02548","last_updated":"2026-04-25T15:22:12Z","snapshot_observed_at":"2026-07-06T20:16:44.607678Z","submitted_at":"2025-01-05T13:59:08Z","title":"Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-23T06:13:26.155588Z"},"links":{"cited_paper":"/paper/2204.12190","citing_paper":"/paper/2501.02548"},"observation_digest":"sha256:141f6d60b76154b7117f104f2b9c9c88e81271cb1f2c9869b1d251c046ccfecd","observation_id":"ad34a5e6-b7a7-40d8-9045-eefbf8e37b53","resolution":{"observed_at":"2026-05-23T06:15:27.558692Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.12190","last_updated":"2022-04-26T09:48:28Z","snapshot_observed_at":"2026-08-16T00:40:11.413099Z","submitted_at":"2022-04-26T09:48:28Z","title":"Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.12190","snapshot_observed_at":"2026-08-05T14:49:53.209213Z","title":"Multi-agent reinforcement learning for traffic signal control through universal communication method","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.20818","last_updated":"2025-08-28T14:16:17Z","snapshot_observed_at":"2026-08-14T05:49:36.890256Z","submitted_at":"2025-08-28T14:16:17Z","title":"cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T14:49:53.209213Z"},"links":{"cited_paper":"/paper/2204.12190","citing_paper":"/paper/2508.20818"},"observation_digest":"sha256:0b593f15f5798e2e06d06ec9b8a386f5c42cabc9e8dd9b3ded6c900f1232de18","observation_id":"f55d1d7c-b757-41a1-a332-1b7e6b8bef4e","resolution":{"observed_at":"2026-08-05T14:49:53.209213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2204.12190/citation-record","integrity":"/paper/2204.12190/integrity","json":"/paper/2204.12190/citation-record.json","paper":"/paper/2204.12190"},"outbound":[],"paper":{"arxiv_id":"2204.12190","last_updated":"2022-04-26T09:48:28Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T00:40:11.413099Z","submitted_at":"2022-04-26T09:48:28Z","title":"Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.12190."}