{"as_of":"2026-08-23T07:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e7f5eec7a16575b21531699c697b11b290aef7129e976a0216cbbd6c8fde714a","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":"2026-08-23T06:30:58.430688+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":"2026-08-08T14:15:43.390034Z","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":"2026-07-04T20:40:07.906014Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.05007","last_updated":"2023-02-10T01:30:01Z","snapshot_observed_at":"2026-08-16T15:56:22.928910Z","submitted_at":"2023-02-10T01:30:01Z","title":"Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05007","snapshot_observed_at":"2026-08-08T14:15:43.390034Z","title":"Scalability Bottlenecks in Multi-Agent Reinforce- ment Learning Systems,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.06963","last_updated":"2026-05-25T01:11:10Z","snapshot_observed_at":"2026-08-13T18:54:48.924163Z","submitted_at":"2025-02-10T19:02:20Z","title":"Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T14:15:43.390034Z"},"links":{"cited_paper":"/paper/2302.05007","citing_paper":"/paper/2502.06963"},"observation_digest":"sha256:a74dab7208be0bd559301e493f0a2f4bfd2582bb0e218d9673a2dbe515ad7c67","observation_id":"fc56e9a1-3f97-48e5-b4ff-388e933486a2","resolution":{"observed_at":"2026-08-08T14:15:43.390034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05007","last_updated":"2023-02-10T01:30:01Z","snapshot_observed_at":"2026-08-16T15:56:22.928910Z","submitted_at":"2023-02-10T01:30:01Z","title":"Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems","version":1},"cited_work":{"arxiv_id":"2302.05007","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.05007","snapshot_observed_at":"2026-07-04T20:40:07.906014Z","title":"Scalability bottlenecks in multi-agent reinforcement learning systems,","venue":null,"work_id":"9feac4c4-0f19-4fd0-87d6-07067480f21a","year":2023},"citing_paper":{"arxiv_id":"2606.25480","last_updated":"2026-06-24T07:10:37Z","snapshot_observed_at":"2026-08-10T10:43:14.409058Z","submitted_at":"2026-06-24T07:10:37Z","title":"Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-25T19:55:31.424515Z"},"links":{"cited_paper":"/paper/2302.05007","citing_paper":"/paper/2606.25480"},"observation_digest":"sha256:e258a92d3e3c5cdfc0dc341da9f29ff7c019115033cd5cc346bcc0564fe1dc5b","observation_id":"c5100255-1511-4acb-8cd4-577d86db9696","resolution":{"observed_at":"2026-07-04T20:40:07.907566Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2302.05007/citation-record","integrity":"/paper/2302.05007/integrity","json":"/paper/2302.05007/citation-record.json","paper":"/paper/2302.05007"},"outbound":[],"paper":{"arxiv_id":"2302.05007","last_updated":"2023-02-10T01:30:01Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-16T15:56:22.928910Z","submitted_at":"2023-02-10T01:30:01Z","title":"Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.05007."}