{"as_of":"2026-08-19T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8fb9a1ffd450a0863258a32dae7e1af0d1b4f7c649201ab8764d113202f8a507","coverage":[{"denominator":1,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T20:43:32.873600Z","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":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.14774/citation-record","integrity":"/paper/2605.14774/integrity","json":"/paper/2605.14774/citation-record.json","paper":"/paper/2605.14774"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.13450","last_updated":"2021-10-26T07:29:16Z","snapshot_observed_at":"2026-08-16T17:46:33.879850Z","submitted_at":"2021-10-26T07:29:16Z","title":"Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control","version":1},"cited_work":{"arxiv_id":"2110.13450","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.13450","snapshot_observed_at":"2026-06-30T20:45:03.603508Z","title":"Namatēvs, I","venue":null,"work_id":"68728d70-cc8b-4249-bbc1-929ef1d2161f","year":2017},"citing_paper":{"arxiv_id":"2605.14774","last_updated":"2026-05-14T12:39:17Z","snapshot_observed_at":"2026-08-06T09:16:33.636309Z","submitted_at":"2026-05-14T12:39:17Z","title":"Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T20:43:32.873600Z"},"links":{"cited_paper":"/paper/2110.13450","citing_paper":"/paper/2605.14774"},"observation_digest":"sha256:2419778a008bb311a2c9c7549c45c617f07d2b43a8a7545b5142392dc0424ab2","observation_id":"c101d891-995b-4e8f-9cd6-b508eca73e86","resolution":{"observed_at":"2026-06-30T20:45:03.605140Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.14774","last_updated":"2026-05-14T12:39:17Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T09:16:33.636309Z","submitted_at":"2026-05-14T12:39:17Z","title":"Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation"},"reference_resolution":{"displayed":1,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":1},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 1 of 1 outbound references and 0 inbound Pith citation observations for arXiv:2605.14774."}