{"as_of":"2026-08-17T08:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e30e6fca9a5a841ce247991e1198f2d177feb81fa969c19c9bfc25d74b844e6d","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:49:28.809890Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2506.18748/citation-record","integrity":"/paper/2506.18748/integrity","json":"/paper/2506.18748/citation-record.json","paper":"/paper/2506.18748"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.469602Z","title":"ITLinQ: A new approach for spectrum sharing in device-to-device communication systems,","venue":null,"work_id":"8fb965a7-a579-489e-82cc-2ba55253d22f","year":2014},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.425492Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:7d350e9bc7aa55cba113ec84524cbd9c7a4a040d652416d4c2a7071a3a4a318c","observation_id":"37111584-822b-4deb-8ac7-2a16169239bb","resolution":{"observed_at":"2026-08-15T18:49:29.474094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.455688Z","title":"ITLinQ+: An improved spectrum sharing mechanism for device-to-device communications,","venue":null,"work_id":"f6e0db58-9e48-4594-8dfd-946e3d2a143e","year":2015},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.475484Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:7643345d25053d9ae62679b478952d311bf0f88048987d6327ce18e535f5f5e2","observation_id":"91c091d3-1ab7-4c83-81b7-8bbac6f18cd2","resolution":{"observed_at":"2026-08-15T18:49:29.460405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.441415Z","title":"FPLinQ: A cooperative spectrum sharing strategy for d2d communications,","venue":null,"work_id":"a2159e68-8990-4ec8-9727-5969648de37c","year":2017},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.563460Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:29b2b2ccd6d09f7d2f0ba5fea493de012d3784fbf9e1425577b4eadc338ea4b7","observation_id":"c26fdc55-2d00-420f-96c4-475cfe220dbd","resolution":{"observed_at":"2026-08-15T18:49:29.445910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.427499Z","title":"Optimal wireless resource alloca- tion with random edge graph neural networks,","venue":null,"work_id":"b605ba73-f7c5-49bb-929a-71cc346ef6d1","year":2020},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.648727Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:2662b2f862fd38de8eb407b2aaf42751d39a4b34e79e87724e21a7423eacab0c","observation_id":"c8f69354-6002-4547-add3-a8f4555a6621","resolution":{"observed_at":"2026-08-15T18:49:29.432163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.413042Z","title":"Resource management in wireless networks via multi- agent deep reinforcement learning,","venue":null,"work_id":"b6a1d3ec-85b0-48ac-ae36-33bfa2385927","year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.684858Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:581e1ad21e4622f58e4490ae853ef630e9401bfa48f26999e8724c3668d2bdfe","observation_id":"2201aa8b-d243-4f58-abdb-a73d9ac838b1","resolution":{"observed_at":"2026-08-15T18:49:29.418312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.08374","last_updated":"2020-05-17T21:20:48Z","snapshot_observed_at":"2026-08-15T01:30:48.078639Z","submitted_at":"2020-05-17T21:20:48Z","title":"Intelligent O-RAN for Beyond 5G and 6G Wireless Networks","version":1},"cited_work":{"arxiv_id":"2005.08374","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.08374","snapshot_observed_at":"2026-08-15T18:49:29.065654Z","title":"Intelligent O-RAN for Beyond 5G and 6G Wireless Networks","venue":"eess.SP","work_id":"e9b67735-ef65-404b-b9cb-751f8abe72dc","year":2020},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.689129Z"},"links":{"cited_paper":"/paper/2005.08374","citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:e727e46da7c4a4dd5179205d61ef6fe8d5b619dc9bf798d18b943715950ec430","observation_id":"9d79d135-a57d-44f5-9f78-2a58a4393b51","resolution":{"observed_at":"2026-08-15T18:49:29.070961Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.399294Z","title":"Unfolding wmmse using graph neural networks for efficient power allocation,","venue":null,"work_id":"3dd741ef-a2e4-44c4-a00c-d5ae958568f2","year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.694262Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:c99a0772a2c350b49d9fd0ef7c903c16cf751bc3e1c6071663abc0e8441ee0e2","observation_id":"27cec773-17dc-487b-a0cb-e1867ba9099a","resolution":{"observed_at":"2026-08-15T18:49:29.403825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.385441Z","title":"Unsupervised learning for asynchronous resource allocation in ad-hoc wireless net- works,","venue":null,"work_id":"2479bddf-d238-4dab-af47-a9abb9fec351","year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.699304Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:fd36a1012f460a8f30fbd78cde6297de75c55db7b2a2982f8d784ac24588be4e","observation_id":"8f327699-66f6-4d34-b226-637c5ef2d743","resolution":{"observed_at":"2026-08-15T18:49:29.389764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.371341Z","title":"Edge artificial intelligence for 6g: Vision, enabling technologies, and appli- cations,","venue":null,"work_id":"4ef898ce-58bf-4027-ad58-e9a3451708f6","year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.704115Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:ea9a2f145f315ed7d447f6db74fca2ed30285cb623775f2c49fd10b1f00b5d08","observation_id":"00f19499-3025-4f1d-8a06-75769cdf03fd","resolution":{"observed_at":"2026-08-15T18:49:29.375928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.357239Z","title":"Link scheduling using graph neural networks,","venue":null,"work_id":"e37b9c49-f857-4966-9dec-c71a176779e8","year":2023},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.709010Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:a94e46346fe65707c670e13b94a210d3c7368894b6f98788482831df2c1f840e","observation_id":"ea9f9ca6-be8b-4945-b749-c38c2cf65e0c","resolution":{"observed_at":"2026-08-15T18:49:29.361625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.13178","last_updated":"2022-05-23T16:15:37Z","snapshot_observed_at":"2026-08-16T18:05:19.999998Z","submitted_at":"2021-08-04T13:06:36Z","title":"Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2108.13178","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.13178","snapshot_observed_at":"2026-08-15T18:49:29.042230Z","title":"Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks","venue":"cs.NI","work_id":"598fc5bf-5612-46e0-9c78-0a269e280062","year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.713801Z"},"links":{"cited_paper":"/paper/2108.13178","citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:aecccd912dc0a1270c5ada8527bd46fc78c50042ec22f69b564dbb14a60c5b93","observation_id":"b78d4831-a8cf-4930-aebc-23fd8fe323d8","resolution":{"observed_at":"2026-08-15T18:49:29.049448Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.343336Z","title":"Power allocation for wireless federated learning using graph neural networks,","venue":null,"work_id":"b18470d3-f48f-47a0-a950-bfe8f96326dd","year":2022},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.718896Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:a922d99fd1ee21c9ed0e8c112436abb0dc9aa1794cb6280e854cf775abebf2be","observation_id":"f6ccf087-ba8b-46ef-a9f8-00153bbbb1cf","resolution":{"observed_at":"2026-08-15T18:49:29.347932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.328526Z","title":"Regularization strategy aided robust unsupervised learning for wireless resource allocation,","venue":null,"work_id":"9e2e502c-42aa-462c-ba84-be105fff4d51","year":2023},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.724289Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:a873bd24a138ee697d3f2b318a58997ab5ab150e863fe904ec6e016b084e98e0","observation_id":"3e0f035f-c941-4ff7-851c-4e6d2014b6a4","resolution":{"observed_at":"2026-08-15T18:49:29.333345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.312844Z","title":"Learning to slice wi-fi networks: A state-augmented primal-dual approach,","venue":null,"work_id":"7b6eb12e-4731-489a-893f-1506e7b3dc15","year":2024},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.729825Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:c3d672c22211933db091b33d64fe4e852bab21025339d2987ef4f220f7b8fb95","observation_id":"c2c42820-8f80-4550-b295-fd84332d979f","resolution":{"observed_at":"2026-08-15T18:49:29.317900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:28.734774Z","title":"Opportunistic routing in wireless communications via learn- able state-augmented policies,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.734774Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:bf6e4b486386d246d2db5e94f536951c67e0b69d12a9a5388f75dc4c167622f3","observation_id":"34a8a738-0443-4c5c-9665-057218ec47fe","resolution":{"observed_at":"2026-08-15T18:49:28.734774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.296664Z","title":"Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems,","venue":null,"work_id":"7006e0d3-6f15-4324-b868-67d5b037476e","year":2025},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.739586Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:26846214622bbea6fdd8f48904e8613ba96bfd245abf2848135ca7187bf5b6f4","observation_id":"99f8f5ab-e736-4182-9fd3-924ba5fae800","resolution":{"observed_at":"2026-08-15T18:49:29.301676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.281720Z","title":"Learning optimal resource allocations in wireless systems,","venue":null,"work_id":"bd46902a-954b-417a-ad50-d0e89d5ba9f5","year":2019},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.743699Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:2f23b767b58fd5a4d168189432f9a739e55c65b2a39d1ae72bf24c0cd6df27f0","observation_id":"96c49452-5502-4b78-ad68-76ddca67414c","resolution":{"observed_at":"2026-08-15T18:49:29.286707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.268017Z","title":"Optimal resource allocation in wireless communi- cation and networking,","venue":null,"work_id":"ec90d99a-2dcb-4116-9453-43cb771fadbb","year":2012},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.747730Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:5b0e864a31810ae3fdf6ebd99d76298a93fd4a64537192990acf25e7337706f8","observation_id":"1f1b685b-9caa-4ebd-93a3-b253efc3d9a7","resolution":{"observed_at":"2026-08-15T18:49:29.272397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.252941Z","title":"Learning resilient radio resource management policies with graph neural net- works,","venue":null,"work_id":"e491ea51-aed5-4157-b0c2-d08f067b18c3","year":2023},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.751516Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:9e7a01b9eb36df17de2726828b6c1c30367872b65fa8a86d2b100d9c74b595b4","observation_id":"4ecb8be8-3545-4aa2-bcbe-9d179b4a6ce6","resolution":{"observed_at":"2026-08-15T18:49:29.257450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11941","last_updated":"2023-09-21T14:36:25Z","snapshot_observed_at":"2026-08-17T01:59:29.033996Z","submitted_at":"2021-02-23T21:07:35Z","title":"State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.11941","snapshot_observed_at":"2026-08-15T18:49:28.755578Z","title":"State augmented constrained reinforcement learning: Overcoming the limitations of learning with rewards,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.755578Z"},"links":{"cited_paper":"/paper/2102.11941","citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:d70eaad83de10f5dd48a58746076d8e2b4ffbc4dc9caa632556379763973f3f6","observation_id":"8e48dd8d-a88f-473c-b4af-1036defc733b","resolution":{"observed_at":"2026-08-15T18:49:28.755578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.239058Z","title":"State- augmented learnable algorithms for resource management in wireless networks,","venue":null,"work_id":"5324a28f-c928-45b9-b197-af8c15332d40","year":2022},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.760028Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:be7896eacc87834a0aa9749c81cc4c2653956456a12386c02f778954af0c94ac","observation_id":"5fe09b80-f96b-4acb-963e-21029d308e79","resolution":{"observed_at":"2026-08-15T18:49:29.243681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:28.763838Z","title":"Boyd and Lieven Vandenberghe, Convex optimization , Cambridge University Press, 2004","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.763838Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:23f970aababf597c665a1d784bd8ae1e3a8e375ac0aa1cbcbf7abf99b343fd3c","observation_id":"e6de9853-da54-460e-99b1-071bf6799f2b","resolution":{"observed_at":"2026-08-15T18:49:28.763838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.215297Z","title":"5, Springer Science & Business Media, 2012","venue":null,"work_id":"b84cd2ac-2513-4a82-80a0-f7f297a878db","year":2012},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.767622Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:e736d3f1fd87959e8b54a30412924fc0a1db1e34deafd44af7da1503d571ce2c","observation_id":"cc1d7920-c939-4cab-85d5-591859512f2b","resolution":{"observed_at":"2026-08-15T18:49:29.219964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.199734Z","title":"Near-optimal solutions of constrained learning problems,","venue":null,"work_id":"ab5cff9c-17ae-42b4-8add-668d7923df2d","year":2024},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.771555Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:33b01d0e939afdb9a7d66459ebb4c1cd4354a4eb8fb7d205f04b9c0421078a24","observation_id":"cb94ebbc-b4d9-4e50-bdc7-d7eff365dcce","resolution":{"observed_at":"2026-08-15T18:49:29.204425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.185152Z","title":"Ergodic stochastic optimization algorithms for wireless communication and networking,","venue":null,"work_id":"98d899c1-c4cd-4341-a494-15d4ee235cab","year":2010},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.775250Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:327df9f781ea369df08e6383f5661686f7d0e7c067a87848b868908fe190a1ee","observation_id":"c70abe03-9e47-450c-953c-e0e9e0fc4bb9","resolution":{"observed_at":"2026-08-15T18:49:29.190007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.169710Z","title":"Inexact stochastic mirror descent for two-stage nonlinear stochastic programs,","venue":null,"work_id":"82623e76-9a91-4bc3-ae00-8ae33a66a5d4","year":2020},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.779045Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:41f5554a00f7283a64a3d6cb3783639f7325fba48432f1c94f5522695fe0a6ff","observation_id":"0bc7b7ac-c764-4df5-a74c-d7c897f13635","resolution":{"observed_at":"2026-08-15T18:49:29.175379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.155701Z","title":"Graph embedding- based wireless link scheduling with few training samples,","venue":null,"work_id":"c99aa30e-38c7-453a-b8a3-f66da8698448","year":2020},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.782885Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:3a4848dd6b11a2cf6e3bc0a7c4251c025ff9eabb52eff131b776c01e85a2eeb0","observation_id":"332960e9-4a86-4c9e-8ea0-d5652c2e69c1","resolution":{"observed_at":"2026-08-15T18:49:29.160215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.08487","last_updated":"2019-07-19T12:38:20Z","snapshot_observed_at":"2026-07-06T08:08:50.115583Z","submitted_at":"2019-07-19T12:38:20Z","title":"A Graph Neural Network Approach for Scalable Wireless Power Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.08487","snapshot_observed_at":"2026-08-15T18:49:28.786788Z","title":"A graph neural network approach for scalable wireless power control,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.786788Z"},"links":{"cited_paper":"/paper/1907.08487","citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:62c032d2332193198dd239391fcecdaa4d45caed845345df2c3cba705206e5c9","observation_id":"f658741e-9c4c-4cd7-99c8-997f8dabe229","resolution":{"observed_at":"2026-08-15T18:49:28.786788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.140462Z","title":"Ultra-dense networks in 5G: Interference management via non- orthogonal multiple access and treating interference as noise,","venue":null,"work_id":"bb3eac1b-a317-42db-a994-33bc7810657f","year":2017},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.791461Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:4f302e2e41a331543e469f794d02370e4657e3b4b1da68866d2681522b361f98","observation_id":"6c4424a7-b872-4359-aaa3-9452cae7b0d2","resolution":{"observed_at":"2026-08-15T18:49:29.145362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.125067Z","title":"A convergence theorem for non negative almost supermartingales and some applications,","venue":null,"work_id":"28b02c38-7192-445e-a27e-02d77d26bc61","year":1985},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.796009Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:1c5a59580995565752ab7c2154009b048ebe3c769247e41a4ecf037c588605ea","observation_id":"6dcd79db-289f-4a2a-a13e-068877589aa0","resolution":{"observed_at":"2026-08-15T18:49:29.130240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.110986Z","title":"Shor, n. z., minimization methods for non-differentiable functions.,","venue":null,"work_id":"fe36ac44-b6a0-4069-af76-43ba9e7d75d2","year":1986},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.800574Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:e6ae7793d321592c1ac8726034689cb79068c8a57ed97d017dd330d0e7a676af","observation_id":"c50bb73d-0a32-4d1b-81dd-e87ee322e8e7","resolution":{"observed_at":"2026-08-15T18:49:29.115451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.096094Z","title":"Distributed network optimization with heuristic rational agents,","venue":null,"work_id":"7e22d8f4-958c-46a9-bbb6-c5b4a12ed51b","year":2012},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.805239Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:c9b417cfc4595a896e16c822d1339198f2440099582a1b7af52ea7920eabbfb6","observation_id":"0ed04481-959f-43b0-a13b-d6dc257266a9","resolution":{"observed_at":"2026-08-15T18:49:29.100813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:49:29.081704Z","title":"Doob, Stochastic Processes, John Wiley & Sons, New York, 1953","venue":null,"work_id":"19f75b0d-c342-40d9-8fc5-0773eae4aee3","year":1953},"citing_paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T18:49:28.809890Z"},"links":{"citing_paper":"/paper/2506.18748"},"observation_digest":"sha256:d481e76017eadaa5eac72f3b6f21137278df463504e1815bbf2136a001b30727","observation_id":"d64b8263-751f-408a-ab94-57b065af7cd1","resolution":{"observed_at":"2026-08-15T18:49:29.086207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.18748","last_updated":"2026-07-23T19:37:25Z","latest_version":2,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-15T18:41:33.498710Z","submitted_at":"2025-06-23T15:20:58Z","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":27},"total_outbound_references":33},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.18748."}