{"as_of":"2026-08-21T12:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2bce8a69d61451539f49fb3b06450333d3febdc21a8d49e08832800b46f14e7","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-05-08T16:39:30.242163Z","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":"2026-05-11T18:06:06.755327Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.05802","last_updated":"2022-06-21T13:47:31Z","snapshot_observed_at":"2026-08-16T18:18:03.505453Z","submitted_at":"2021-06-10T15:09:33Z","title":"Metric Policy Representations for Opponent Modeling","version":2},"cited_work":{"arxiv_id":"2106.05802","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.05802","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2106.05802 , year =","venue":null,"work_id":"dbe2e96d-02c5-4e1c-91c1-02df9dc89b49","year":null},"citing_paper":{"arxiv_id":"2605.05020","last_updated":"2026-05-06T15:18:42Z","snapshot_observed_at":"2026-07-31T14:41:54.048350Z","submitted_at":"2026-05-06T15:18:42Z","title":"Graph-SND: Sparse Aggregation for Behavioral Diversity in Multi-Agent Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-08T16:39:30.242163Z"},"links":{"cited_paper":"/paper/2106.05802","citing_paper":"/paper/2605.05020"},"observation_digest":"sha256:ad97bceb40c27d419480fe9c0ce6627b693fc6aa1349e66d8c29f8229f542116","observation_id":"5fbc08bb-1c64-486c-a1a8-f8841a16aa04","resolution":{"observed_at":"2026-05-11T18:06:06.757478Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2106.05802/citation-record","integrity":"/paper/2106.05802/integrity","json":"/paper/2106.05802/citation-record.json","paper":"/paper/2106.05802"},"outbound":[],"paper":{"arxiv_id":"2106.05802","last_updated":"2022-06-21T13:47:31Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T18:18:03.505453Z","submitted_at":"2021-06-10T15:09:33Z","title":"Metric Policy Representations for Opponent Modeling"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2106.05802."}