{"as_of":"2026-08-11T18:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9113fbbfb2d0b34d09ba533bd5d88bb4fe85229bd4ccdfc2549c177fd6857ac4","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":"2026-08-11T06:34:44.6726+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":"2026-08-10T23:21:30.212325Z","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":"2026-08-10T23:21:30.962044Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.09785","last_updated":"2021-09-09T15:37:37Z","snapshot_observed_at":"2026-07-06T11:01:58.137141Z","submitted_at":"2021-04-20T06:51:50Z","title":"Model-predictive control and reinforcement learning in multi-energy system case studies","version":2},"cited_work":{"arxiv_id":"2104.09785","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.09785","snapshot_observed_at":"2026-08-10T23:21:30.962044Z","title":"Model-predictive control and reinforcement learning in multi-energy system case studies","venue":"eess.SY","work_id":"bc1cedce-fb5e-49fe-aa54-ee5a5e9290ec","year":2021},"citing_paper":{"arxiv_id":"2412.20946","last_updated":"2025-07-15T07:46:57Z","snapshot_observed_at":"2026-08-10T23:03:48.791836Z","submitted_at":"2024-12-30T13:38:31Z","title":"Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T23:21:30.212325Z"},"links":{"cited_paper":"/paper/2104.09785","citing_paper":"/paper/2412.20946"},"observation_digest":"sha256:2f5ce519ef44a6d2b52003e5cd1d1ec80898109819c9aa270d2817b2b00e8fba","observation_id":"89e11db6-c783-4221-b2ad-b8f8800c5d01","resolution":{"observed_at":"2026-08-10T23:21:30.966466Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2104.09785/citation-record","integrity":"/paper/2104.09785/integrity","json":"/paper/2104.09785/citation-record.json","paper":"/paper/2104.09785"},"outbound":[],"paper":{"arxiv_id":"2104.09785","last_updated":"2021-09-09T15:37:37Z","latest_version":2,"primary_category":"eess.SY","snapshot_observed_at":"2026-07-06T11:01:58.137141Z","submitted_at":"2021-04-20T06:51:50Z","title":"Model-predictive control and reinforcement learning in multi-energy system case studies"},"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 1 inbound Pith citation observation for arXiv:2104.09785."}