{"as_of":"2026-08-08T04:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9017749f41ebfb0146ead2232de1dd1b2d81f5bcc86ae6f7e3ee7574a83aa727","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T16:45:55.178372Z","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":"2026-06-29T20:53:58.327292Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.07211","last_updated":"2025-02-11T03:09:45Z","snapshot_observed_at":"2026-07-06T20:34:29.976207Z","submitted_at":"2025-02-11T03:09:45Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07211","snapshot_observed_at":"2026-08-06T16:45:55.178372Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.178372Z"},"links":{"cited_paper":"/paper/2502.07211","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:f5c704c694c5ecd654fb908db3bb191082f829e69b010f0406711ab029a50f5d","observation_id":"bc443023-c0cc-4966-b735-b6a143f55e99","resolution":{"observed_at":"2026-08-06T16:45:55.178372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07211","last_updated":"2025-02-11T03:09:45Z","snapshot_observed_at":"2026-07-06T20:34:29.976207Z","submitted_at":"2025-02-11T03:09:45Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":"2502.07211","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.07211","snapshot_observed_at":"2026-06-29T20:53:58.327292Z","title":"Improve the training efficiency of drl for wireless communication resource allocation: The role of generative diffusion models,","venue":null,"work_id":"1b765d01-0735-4cfd-af4f-68184de73e3f","year":2025},"citing_paper":{"arxiv_id":"2605.25531","last_updated":"2026-05-25T07:37:39Z","snapshot_observed_at":"2026-08-05T10:02:09.642060Z","submitted_at":"2026-05-25T07:37:39Z","title":"From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T20:49:07.030872Z"},"links":{"cited_paper":"/paper/2502.07211","citing_paper":"/paper/2605.25531"},"observation_digest":"sha256:83258dea9ae6bfbce0fe2b2a4f0226896604459847741ff032081675ddeb93f9","observation_id":"29967abd-53e5-4e22-86f0-ea0480e7e350","resolution":{"observed_at":"2026-06-29T20:53:58.328893Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07211","last_updated":"2025-02-11T03:09:45Z","snapshot_observed_at":"2026-07-06T20:34:29.976207Z","submitted_at":"2025-02-11T03:09:45Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07211","snapshot_observed_at":"2026-08-01T19:56:12.280337Z","title":"Improve the training efficiency of DRL for wireless communication resource allocation: The role of generative diffusion models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16812","last_updated":"2026-07-18T13:15:10Z","snapshot_observed_at":"2026-08-06T17:25:10.272277Z","submitted_at":"2026-07-18T13:15:10Z","title":"Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-01T19:56:12.280337Z"},"links":{"cited_paper":"/paper/2502.07211","citing_paper":"/paper/2607.16812"},"observation_digest":"sha256:e4b05f4fbd2a03c03e12feaa0f4358555e8991a1fdb3f85bc52b55152c59d771","observation_id":"5ebef4e4-9ea0-422d-9616-7e7856fbb817","resolution":{"observed_at":"2026-08-01T19:56:12.280337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.07211/citation-record","integrity":"/paper/2502.07211/integrity","json":"/paper/2502.07211/citation-record.json","paper":"/paper/2502.07211"},"outbound":[],"paper":{"arxiv_id":"2502.07211","last_updated":"2025-02-11T03:09:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T20:34:29.976207Z","submitted_at":"2025-02-11T03:09:45Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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:2502.07211."}