{"as_of":"2026-08-08T15:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e5ae183481cb2453e79b011a87688de877f950f2a0b87d5fbcf1d44f20f77e5e","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:48:17.832337Z","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":"2026-06-29T16:23:39.682751Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2301.05334","last_updated":"2023-01-13T00:07:08Z","snapshot_observed_at":"2026-07-06T14:40:53.980335Z","submitted_at":"2023-01-13T00:07:08Z","title":"TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems","version":1},"cited_work":{"arxiv_id":"2301.05334","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.05334","snapshot_observed_at":"2026-06-29T16:23:39.682751Z","title":"Transfqmix: Transformers for leveraging the graph structure of multi-agent reinforcement learning problems","venue":null,"work_id":"42385b2d-d775-42f2-8375-8c7e7ccf3061","year":2023},"citing_paper":{"arxiv_id":"2502.00558","last_updated":"2025-02-13T22:50:50Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-01T21:04:32Z","title":"Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-23T03:34:58.917609Z"},"links":{"cited_paper":"/paper/2301.05334","citing_paper":"/paper/2502.00558"},"observation_digest":"sha256:97dd015fe22f9526f13f10c6fc111386a3eba913d8b37bc1b54f3a1721f9f8f6","observation_id":"e6a32de1-4fab-4249-9f6e-12534fc2fd1e","resolution":{"observed_at":"2026-05-23T03:35:20.728168Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05334","last_updated":"2023-01-13T00:07:08Z","snapshot_observed_at":"2026-07-06T14:40:53.980335Z","submitted_at":"2023-01-13T00:07:08Z","title":"TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05334","snapshot_observed_at":"2026-08-06T18:48:17.832337Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07302","last_updated":"2025-07-09T22:01:32Z","snapshot_observed_at":"2026-08-07T05:56:16.811721Z","submitted_at":"2025-07-09T22:01:32Z","title":"Application of LLMs to Multi-Robot Path Planning and Task Allocation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T18:48:17.832337Z"},"links":{"cited_paper":"/paper/2301.05334","citing_paper":"/paper/2507.07302"},"observation_digest":"sha256:199558cd60476032b17a3cb45f875398080f5fc553f0422d3a42c1c0909adcc6","observation_id":"4fe27134-b04e-45e8-9654-655a59bd277b","resolution":{"observed_at":"2026-08-06T18:48:17.832337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05334","last_updated":"2023-01-13T00:07:08Z","snapshot_observed_at":"2026-07-06T14:40:53.980335Z","submitted_at":"2023-01-13T00:07:08Z","title":"TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems","version":1},"cited_work":{"arxiv_id":"2301.05334","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.05334","snapshot_observed_at":"2026-06-29T16:23:39.682751Z","title":"Transfqmix: Transformers for leveraging the graph structure of multi-agent reinforcement learning problems","venue":null,"work_id":"42385b2d-d775-42f2-8375-8c7e7ccf3061","year":2023},"citing_paper":{"arxiv_id":"2606.05208","last_updated":"2026-05-26T06:44:07Z","snapshot_observed_at":"2026-07-06T23:45:11.627867Z","submitted_at":"2026-05-26T06:44:07Z","title":"Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-29T16:14:25.741686Z"},"links":{"cited_paper":"/paper/2301.05334","citing_paper":"/paper/2606.05208"},"observation_digest":"sha256:5cf3b96026bffe9e82e5e23630b84a162ef8bac1c7d20c834401c86cec060156","observation_id":"0b624126-07e8-46a3-a810-02aabf12244e","resolution":{"observed_at":"2026-06-29T16:23:39.684176Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2301.05334/citation-record","integrity":"/paper/2301.05334/integrity","json":"/paper/2301.05334/citation-record.json","paper":"/paper/2301.05334"},"outbound":[],"paper":{"arxiv_id":"2301.05334","last_updated":"2023-01-13T00:07:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:40:53.980335Z","submitted_at":"2023-01-13T00:07:08Z","title":"TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.05334."}