{"as_of":"2026-08-11T05:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce4b19d3427ba2896b20d54d389a4ea5810dfeac9db5837340fa75d326e151fc","coverage":[{"denominator":87,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":87,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:40:59.073705Z","state":"measured"},{"denominator":88,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":88,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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":"2026-08-06T05:35:40.449647Z","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":"2026-08-06T05:35:41.926170Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"cited_work":{"arxiv_id":"2501.12942","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.12942","snapshot_observed_at":"2026-08-06T05:35:41.926170Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","venue":"cs.AI","work_id":"043cb37f-0417-4dd4-be6e-456073e8685a","year":2025},"citing_paper":{"arxiv_id":"2508.01589","last_updated":"2025-08-03T05:06:26Z","snapshot_observed_at":"2026-08-09T01:25:10.392992Z","submitted_at":"2025-08-03T05:06:26Z","title":"Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences","version":1},"reference_index":222,"source":"pdf_text","source_observed_at":"2026-08-06T05:35:40.449647Z"},"links":{"cited_paper":"/paper/2501.12942","citing_paper":"/paper/2508.01589"},"observation_digest":"sha256:6dbd5b16e80af2d33a9f5434073fdcee841c874b1cb73dd2942562237d53d7f1","observation_id":"b4528eaa-c0ac-4312-ab8e-bea7a54b166c","resolution":{"observed_at":"2026-08-06T05:35:41.976756Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.12942/citation-record","integrity":"/paper/2501.12942/integrity","json":"/paper/2501.12942/citation-record.json","paper":"/paper/2501.12942"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.15657","last_updated":"2023-07-10T07:25:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-11-28T18:59:02Z","title":"Is Conditional Generative Modeling all you need for Decision-Making?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15657","snapshot_observed_at":"2026-08-10T16:40:58.578102Z","title":"Is conditional generative modeling all you need for decision-making? arXiv preprint arXiv:2211.15657, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.578102Z"},"links":{"cited_paper":"/paper/2211.15657","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:b89ff097cb1e6a4589f3b28ff745daa4137598edd17d100cacf8fc182499792b","observation_id":"eb104c3c-88a2-4b61-944c-d205dc9f00b3","resolution":{"observed_at":"2026-08-10T16:40:58.578102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04088","last_updated":"2025-01-16T09:07:51Z","snapshot_observed_at":"2026-08-11T01:49:03.849769Z","submitted_at":"2024-06-06T13:58:41Z","title":"Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2406.04088","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.04088","snapshot_observed_at":"2026-08-10T16:40:59.503166Z","title":"Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning","venue":"cs.LG","work_id":"65546052-ff71-4a39-aea0-a2faec16b9ef","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.585733Z"},"links":{"cited_paper":"/paper/2406.04088","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:b4c328254244c67502fea8a044da91f1b37b675baa28de9f59fb96bc14adf439","observation_id":"36099542-422f-4018-8574-f77ac7206ef5","resolution":{"observed_at":"2026-08-10T16:40:59.511070Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:58.592310Z","title":"Lyapunov- based optimization of edge resources for energy-efficient adaptive federated learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.592310Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:3f14089acbc68166e9ca8ab1c2d27976a2539d9ee203ea875f10e29918706e40","observation_id":"4827e75b-8caa-4d6a-8737-e24bd90971d5","resolution":{"observed_at":"2026-08-10T16:40:58.592310Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.598271Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.598271Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:716b27cae0fadbb17dda7378bcc2d28bb626390e744209e478a8d1f452f0f8e2","observation_id":"0484aaf9-3369-43cc-897c-b64c00b33c5e","resolution":{"observed_at":"2026-08-10T16:40:58.598271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-08-10T16:40:58.603977Z","title":"Score regularized policy optimization through diffusion behavior","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.603977Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:167568549b232abe661f64580031fb5d5d8510894a64eaa3f3f8aeb387b8d730","observation_id":"bbfce0c0-7a29-4e1c-83a6-0308dc660d6a","resolution":{"observed_at":"2026-08-10T16:40:58.603977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14548","last_updated":"2023-02-28T04:19:48Z","snapshot_observed_at":"2026-08-08T12:55:11.002893Z","submitted_at":"2022-09-29T04:36:23Z","title":"Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14548","snapshot_observed_at":"2026-08-10T16:40:58.616367Z","title":"Offline reinforcement learn- ing via high-fidelity generative behavior modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.616367Z"},"links":{"cited_paper":"/paper/2209.14548","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:6e5bb19b165603dd5e80fb3b61960ae815d447371b36aeab68e86e93bde43529","observation_id":"74727034-1759-49c4-b9c2-884942bb6f24","resolution":{"observed_at":"2026-08-10T16:40:58.616367Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.621934Z","title":"Timely-throughput optimal scheduling with prediction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.621934Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:be1e051c05df1ec327d3acdb135ab8d8c7ca26577577941d8d2fbd051ec8f44c","observation_id":"8af82131-b180-4117-82e0-606d8b8fda7f","resolution":{"observed_at":"2026-08-10T16:40:58.621934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07194","last_updated":"2023-02-14T17:02:35Z","snapshot_observed_at":"2026-07-06T14:51:48.920020Z","submitted_at":"2023-02-14T17:02:35Z","title":"Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07194","snapshot_observed_at":"2026-08-10T16:40:58.627339Z","title":"Score approximation, esti- mation and distribution recovery of diffusion models on low-dimensional data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.627339Z"},"links":{"cited_paper":"/paper/2302.07194","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:4c694dd48fb01058180135f46e73e98992535d41664febbcc540c4b8d95da40d","observation_id":"0d10936d-99d9-4224-a98b-383dc5179e76","resolution":{"observed_at":"2026-08-10T16:40:58.627339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19690","last_updated":"2024-10-31T18:09:38Z","snapshot_observed_at":"2026-08-02T14:04:10.719542Z","submitted_at":"2024-05-30T05:04:33Z","title":"Diffusion Policies creating a Trust Region for Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19690","snapshot_observed_at":"2026-08-10T16:40:58.633334Z","title":"Diffusion policies creating a trust region for offline reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.633334Z"},"links":{"cited_paper":"/paper/2405.19690","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:538c15eb476f38be5bef0dd81545006973eb4eff7b6f532b834fe9ad101b2504","observation_id":"5c080717-96c1-40f2-be92-b41c935109f4","resolution":{"observed_at":"2026-08-10T16:40:58.633334Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.639229Z","title":"The roles of carbon capture, utilization and storage in the transition to a low-carbon energy system using a stochastic optimal scheduling approach","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.639229Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:db3362c738e69fede59d66e347b69d5ca6c660ecd99312f203be812559918fcb","observation_id":"e4e56800-408c-489e-8eea-e6053d96c63c","resolution":{"observed_at":"2026-08-10T16:40:58.639229Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.644959Z","title":"Channel estimation for extremely large-scale mimo: Far-field or near-field? IEEE Transactions on Communications , 70(4):2663–2677, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.644959Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:b5da0394a7c95576aecb9f724e887d4dc38927705d8068bfdfaa5e9c2f9738c7","observation_id":"b3c2562d-37d5-4b91-b8be-16b7da7ce3e9","resolution":{"observed_at":"2026-08-10T16:40:58.644959Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.650354Z","title":"Data center energy consumption modeling: A survey","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.650354Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:d5a81b079d3b7b518a275fe7f817971eb03617ddecb3f95492089becc8f2b19d","observation_id":"faed407b-ab62-4259-a36f-c5b8fcd219b8","resolution":{"observed_at":"2026-08-10T16:40:58.650354Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.655490Z","title":"Channel-aware earliest deadline due fair schedul- ing for wireless multimedia networks.Wireless Personal Communications, 38(2):233–252, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.655490Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:ba61aaada452b58f5288e205baed238f4eed2e997f6c6cf622df17b0bfa7bc80","observation_id":"1103ebb0-abe9-4bb4-8f85-3cf1319db7ac","resolution":{"observed_at":"2026-08-10T16:40:58.655490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12557","last_updated":"2025-06-23T14:26:35Z","snapshot_observed_at":"2026-08-11T03:25:10.354390Z","submitted_at":"2024-10-16T13:34:40Z","title":"One Step Diffusion via Shortcut Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12557","snapshot_observed_at":"2026-08-10T16:40:58.660911Z","title":"One step diffusion via shortcut models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.660911Z"},"links":{"cited_paper":"/paper/2410.12557","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:a55364bd64d9a42b23fb64cf1683797869746401f0ef2db45d89d9f8fb17a638","observation_id":"1d4a351a-c3dd-4cea-ad12-0857b3731924","resolution":{"observed_at":"2026-08-10T16:40:58.660911Z","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":"2026-08-10T16:41:00.372356Z","title":"A minimalist approach to offline reinforcement learn- ing","venue":null,"work_id":"d0275e5f-771f-44bb-9076-839c29122f07","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.666401Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:ae137175c1057e1ec57afe6403f695c602ce17dac308462bcad8286e62b0b5f4","observation_id":"0d44e707-aa40-4cb5-bdab-883327d7ad08","resolution":{"observed_at":"2026-08-10T16:41:00.377262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.357116Z","title":"Off-policy deep reinforcement learning with- out exploration","venue":null,"work_id":"dfa4a506-7d05-4917-a438-4ae3744aea6f","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.671646Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:7882d72f9de1a5773afd44383e5b3ad3a1a8cd697731440d3af46acb27394968","observation_id":"f3a3ebed-635a-4c63-bd92-08d10a7bbe7e","resolution":{"observed_at":"2026-08-10T16:41:00.362017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.341080Z","title":"Channel estimation for extremely large-scale massive mimo systems","venue":null,"work_id":"6a93fa8a-51a8-435b-a797-c473718ca60f","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.677392Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:540a8094680e8e77e70e58029871fa8ad70f57b380945c64c3fc448c69b77096","observation_id":"4bc7035a-edd4-4c7c-b780-fc8c49b71bdc","resolution":{"observed_at":"2026-08-10T16:41:00.346376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.10573","last_updated":"2023-05-19T18:31:04Z","snapshot_observed_at":"2026-07-06T15:18:07.416392Z","submitted_at":"2023-04-20T18:04:09Z","title":"IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.10573","snapshot_observed_at":"2026-08-10T16:40:58.682727Z","title":"Idql: Implicit q-learning as an actor-critic method with diffusion policies","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.682727Z"},"links":{"cited_paper":"/paper/2304.10573","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:73bbdac0de993dedd035ffd990b595e510846b04111d60f48c1f61b7e080aa6e","observation_id":"6f7444df-37e3-41af-9a6a-0786ccc7060f","resolution":{"observed_at":"2026-08-10T16:40:58.682727Z","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":"2026-08-10T16:41:00.325782Z","title":"Double q-learning","venue":null,"work_id":"484f12d9-e61d-4340-bbf4-7391db73e903","year":2010},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.688194Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:e7b328968f80a97a6a6f553e0d12c984aabbea56a6c6102c9e39bca149fd92ee","observation_id":"d456b656-edd8-41d9-bf4e-3fd8decce686","resolution":{"observed_at":"2026-08-10T16:41:00.330463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:58.693830Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.693830Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:bdc4d8282108006b739484d8e758975d26c96dc9f0188df2f874a50d506a1cc7","observation_id":"89bebf86-f693-4666-b5a8-d43c9c49fec6","resolution":{"observed_at":"2026-08-10T16:40:58.693830Z","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":"2026-08-10T16:41:00.299975Z","title":"Multi-user delay- constrained scheduling with deep recurrent reinforcement learning","venue":null,"work_id":"3e3f71ea-4d41-4c86-89e6-da20e0438f80","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.699441Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:d544f4e3a6619bd9f406aa78a34a8438ca0caba308b19f65aaf53c70ddba2da6","observation_id":"e887b3d5-eff8-467f-bdb7-330c0805e2ed","resolution":{"observed_at":"2026-08-10T16:41:00.304848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.284532Z","title":"When backpressure meets predictive scheduling","venue":null,"work_id":"83b343e0-8057-47c9-9c61-01661311bf00","year":2015},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.704782Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:529d92faf741e6e81b73312078629be56b39b44d9408f7c1efec20f6e7a1b7d3","observation_id":"26ef9fee-dfbe-40a6-9f29-86d5ed30babe","resolution":{"observed_at":"2026-08-10T16:41:00.289529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.09991","last_updated":"2022-12-21T01:06:18Z","snapshot_observed_at":"2026-08-06T05:32:02.292779Z","submitted_at":"2022-05-20T07:02:03Z","title":"Planning with Diffusion for Flexible Behavior Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.09991","snapshot_observed_at":"2026-08-10T16:40:58.710473Z","title":"Planning with diffusion for flexible behavior synthesis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.710473Z"},"links":{"cited_paper":"/paper/2205.09991","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:c833e7d882a9e6ea9b9f9085376700edeebb8d79162d267a13869d102cf8d5bd","observation_id":"d9978b55-8426-4dac-8dda-d9ddf05f7280","resolution":{"observed_at":"2026-08-10T16:40:58.710473Z","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":"2026-08-10T16:41:00.269450Z","title":"A review of power consumption models of servers in data centers","venue":null,"work_id":"d74decde-a917-4a6c-b243-4a74188e08cb","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.716235Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:a2723587ef314ab118db0bea86639e12dda8f71198407411eda6c10c684d021f","observation_id":"e1b04afe-d38c-46ff-8339-9dc2bb8820a4","resolution":{"observed_at":"2026-08-10T16:41:00.274376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.253998Z","title":"Joint message-passing and convex optimization framework for energy-efficient surveillance uav scheduling","venue":null,"work_id":"68234fa1-0a83-497f-9c1f-7964fc38baf0","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.723006Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:edef5517ecb3cc15c80c05e8df9f16a32653b25ef8bbd7fda27f1808cfe8850a","observation_id":"fbaff9f2-2a9a-40f6-b029-e1fe6a3b0c1b","resolution":{"observed_at":"2026-08-10T16:41:00.258883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.239332Z","title":"Factors influencing user satisfaction with information systems: A systematic review","venue":null,"work_id":"aa9a1dfd-c521-44f9-868a-1fff4533360a","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.729342Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:21f2ec3de9b047f71347e6ad9b9380faf71a57e45811d58ac01ffca8de3fd40a","observation_id":"7b0ea0a7-8470-4233-a8d5-66d4da652555","resolution":{"observed_at":"2026-08-10T16:41:00.244177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.223767Z","title":"Efficient diffusion policies for offline reinforcement learning","venue":null,"work_id":"68864932-04e9-4891-8881-0867070ba259","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.734764Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:bc3351b8869e08ac1092c524888803b6c818351bebec5c621fdc3afdace54aca","observation_id":"654d3078-4350-4a1f-a2a3-ee14c9ac217b","resolution":{"observed_at":"2026-08-10T16:41:00.229034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.209019Z","title":"Understanding diffusion objectives as the elbo with simple data augmentation","venue":null,"work_id":"69f5973a-f897-4b71-97de-92e6e483d936","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.740296Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:38ef517cd0d4aabd2df403ac9c4c0598a320a2e7646d661637b0dd11c6854efd","observation_id":"3b6f7418-db3f-4326-903a-dbde703a09ed","resolution":{"observed_at":"2026-08-10T16:41:00.213827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.194072Z","title":"Variational diffusion models","venue":null,"work_id":"b04e73f1-2bd9-4e20-b2b3-8481f2378e5e","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.745949Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:192f0cc14137e1d69f90f2c9687f080bf4a5db6408c7f4fb13f14867bb548589","observation_id":"ac142bea-a9d7-49be-b880-8c11c7855f2f","resolution":{"observed_at":"2026-08-10T16:41:00.198780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-10T16:40:58.752517Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.752517Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:a60d0b03a2ddf4db999b2323ac807148d14552bd9b5611be0a735ae968a92643","observation_id":"d9e344b9-e39f-409a-a729-65b7af7b2344","resolution":{"observed_at":"2026-08-10T16:40:58.752517Z","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":"2026-08-10T16:41:00.178874Z","title":"Optimization energy consumption with multiple mobile sinks using fuzzy logic in wireless sensor networks","venue":null,"work_id":"ef936ea0-a606-400d-9cc7-858ff805cd08","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.758495Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:63806d7d98638a542caffec9696327e41a834d7eb1776287b5e7461b9a9c4659","observation_id":"acd96393-adb9-462a-be53-5e87cfda4227","resolution":{"observed_at":"2026-08-10T16:41:00.183705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.163949Z","title":"Offline reinforcement learn- ing with fisher divergence critic regularization","venue":null,"work_id":"35f0498b-3b37-40ae-a929-cb7524c84373","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.763909Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:7cdd25520649d493e950ce6da79a675edc53c1c80ad643749724fe67cb717fe1","observation_id":"672d1320-f40f-4d34-b1d0-4503b38792b7","resolution":{"observed_at":"2026-08-10T16:41:00.168904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06169","last_updated":"2021-10-12T17:05:05Z","snapshot_observed_at":"2026-08-06T15:42:21.967989Z","submitted_at":"2021-10-12T17:05:05Z","title":"Offline Reinforcement Learning with Implicit Q-Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06169","snapshot_observed_at":"2026-08-10T16:40:58.770062Z","title":"Offline reinforcement learning with implicit q-learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.770062Z"},"links":{"cited_paper":"/paper/2110.06169","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:2576df245a377df4fa0f76fafc9ddf1140bf3e93d2258cabbf47d5ca9c9968e0","observation_id":"d8e7385d-8502-470f-a2ca-5b12e7b3c5c0","resolution":{"observed_at":"2026-08-10T16:40:58.770062Z","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":"2026-08-10T16:41:00.148470Z","title":"Stabilizing off- policy q-learning via bootstrapping error reduction.Advances in Neural Information Processing Systems, 32, 2019","venue":null,"work_id":"008dd88b-ccdf-4dcf-a821-d16ad153bb1a","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.776041Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:7985dbe1b1c85ecd3d47c6d12a09b5d167a29cce6ae8af65dfda52e8cc169698","observation_id":"3c62c3c6-bd08-47b8-b207-1267545e8b04","resolution":{"observed_at":"2026-08-10T16:41:00.153687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.132312Z","title":"Conservative q-learning for offline reinforcement learning","venue":null,"work_id":"65b5baa7-09a9-4e2d-b32f-e91388cfe6f8","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.781584Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:2c103e00191a140c777273a9589b4e176a05da13a614ee1dbe4650143cadfd0c","observation_id":"1ddf8937-d414-44c8-a2cc-31856573c654","resolution":{"observed_at":"2026-08-10T16:41:00.137391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.117107Z","title":"Batch reinforcement learning","venue":null,"work_id":"82c29d61-bc03-4dab-ae1e-631aa4d35742","year":2012},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.786942Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:cb354a6d143afd1f841f36a045b0fe1f2febe5646bffc72330a264e5462b4f39","observation_id":"38192390-e2ee-44c9-ab8a-1814ded3ad23","resolution":{"observed_at":"2026-08-10T16:41:00.122001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.100001Z","title":"Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems","venue":null,"work_id":"851ee14a-8734-4fa6-84a6-302d45db5f04","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.792094Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:f953cfecb90909ad2e99483deb0b82426e60a9e4d976f69f560dfc33d1189daf","observation_id":"cc3b6395-d751-462e-8673-4a0b431a6ecb","resolution":{"observed_at":"2026-08-10T16:41:00.105830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01643","last_updated":"2020-11-01T23:50:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-04T17:00:15Z","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.01643","snapshot_observed_at":"2026-08-10T16:40:58.797435Z","title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.797435Z"},"links":{"cited_paper":"/paper/2005.01643","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:23cd6120421ec594eafcc5ac85c8c113e985be296b49d2f5ccec6b3de4275c1c","observation_id":"2dacc0a2-94b0-43fd-b8f6-cfa10a5dc082","resolution":{"observed_at":"2026-08-10T16:40:58.797435Z","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":"2026-08-10T16:41:00.083099Z","title":"Low-carbon optimal learning scheduling of the power system based on carbon capture system and carbon emission flow theory","venue":null,"work_id":"100d67ec-c261-4276-9f9f-442b538d81bf","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.802727Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:6f480a2592b528a4ed887aed4e76874a258ab6fa69b4533ef1a5b21d0e23cc9e","observation_id":"a1928940-b3ac-4414-9416-21b3b53b738b","resolution":{"observed_at":"2026-08-10T16:41:00.088182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.067598Z","title":"Delay-aware vnf scheduling: A rein- forcement learning approach with variable action set","venue":null,"work_id":"66d3ebc1-6b08-4810-a2a2-76405ba4a9ad","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.807989Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:f80bb869738e723a9e3aca33e79aa78dbff8c6ed171b6aa0fb5e765cdd975737","observation_id":"bdd5e070-d793-436b-a6ac-f2bbe715c980","resolution":{"observed_at":"2026-08-10T16:41:00.072716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02429","last_updated":"2025-01-13T14:58:30Z","snapshot_observed_at":"2026-08-08T11:50:26.930173Z","submitted_at":"2024-02-04T09:58:42Z","title":"Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02429","snapshot_observed_at":"2026-08-10T16:40:58.813895Z","title":"To- wards an information theoretic framework of context-based offline meta-reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.813895Z"},"links":{"cited_paper":"/paper/2402.02429","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:cd6fd42d5f83b100f143ea82d6e90ee42a87b61f202fd89a052beb377f7fa9f7","observation_id":"716d4f33-3d6a-4541-b088-438ae89425ae","resolution":{"observed_at":"2026-08-10T16:40:58.813895Z","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":"2026-08-10T16:41:00.051473Z","title":"Offline learning-based multi-user delay-constrained scheduling","venue":null,"work_id":"14a47401-5a62-42a4-9343-303274c88e10","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.819785Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:146a2cbbd573dfd217fcc897fadb9b82398ec4f7542e6b5f28c49dde2519b7cc","observation_id":"e1bdf852-3303-467d-9218-25a062571e51","resolution":{"observed_at":"2026-08-10T16:41:00.056482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:41:00.033667Z","title":"Learning to schedule tasks with deadline and throughput constraints","venue":null,"work_id":"914f2687-3cff-4946-a3e8-52ba9a78f03d","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.825320Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:e50271959439f9e52022561bea9b6fdec6d8c95a3dc45df464ac365060cc6bc2","observation_id":"edc0878e-8664-48fc-9b74-1918e49aa937","resolution":{"observed_at":"2026-08-10T16:41:00.038843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.08473","last_updated":"2019-07-06T05:30:14Z","snapshot_observed_at":"2026-07-06T07:46:50.846147Z","submitted_at":"2019-04-17T19:46:02Z","title":"Off-Policy Policy Gradient with State Distribution Correction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.08473","snapshot_observed_at":"2026-08-10T16:40:58.830677Z","title":"Off-policy policy gradient with state distribution correction","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.830677Z"},"links":{"cited_paper":"/paper/1904.08473","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:76c4b71b1590e51c2f88c3dd63fc4fb952ce68e1725f03b6a39850ba3c32ccb4","observation_id":"13a0e78d-5d79-4644-a5df-13194e1e92ac","resolution":{"observed_at":"2026-08-10T16:40:58.830677Z","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":"2026-08-10T16:41:00.016271Z","title":null,"venue":null,"work_id":"ee84f971-debb-4eaf-9f52-bc8cfbde3b1c","year":2018},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.836197Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:3aa12159e858b8cc9f74b117c0f1180924558980912f7a4eb42f9d1c366f4e32","observation_id":"cc15a9e8-b67c-423d-a4f3-e712625eb00d","resolution":{"observed_at":"2026-08-10T16:41:00.021500Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12824","last_updated":"2023-05-30T13:15:39Z","snapshot_observed_at":"2026-08-05T01:45:07.925531Z","submitted_at":"2023-04-25T13:50:41Z","title":"Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12824","snapshot_observed_at":"2026-08-10T16:40:58.841613Z","title":"Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.841613Z"},"links":{"cited_paper":"/paper/2304.12824","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:d5a1a3446718cb2b89a558f3bf9f3413dbed245232ffd2b08f823664cfb443ef","observation_id":"1808da20-46da-4dda-977d-0d80b12d9684","resolution":{"observed_at":"2026-08-10T16:40:58.841613Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:58.846992Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.846992Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:52ea0587f81b72ee8ca49d6bc4490d7cc2afb0294c4149d9a75fb22c790cb2bf","observation_id":"7d699583-982f-4f7d-b994-5376fdfd6079","resolution":{"observed_at":"2026-08-10T16:40:58.846992Z","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":"2026-08-10T16:40:59.985707Z","title":"Contract and lyapunov optimization-based load scheduling and energy management for uav charging stations","venue":null,"work_id":"a7a78b63-71d5-4f4e-8621-5bccac2045ac","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.851959Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:afbada12efcfec2ed47bae3dfb1c3a52d5e3b56506f244f594636b1f224e9008","observation_id":"2b3ec48e-a301-407f-ba99-194f8a084100","resolution":{"observed_at":"2026-08-10T16:40:59.991204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.969050Z","title":"Iteratively refined behavior regularization for offline reinforcement learning","venue":null,"work_id":"c4f5c89d-1a31-4597-bb21-a1d294a58710","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.857824Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:2df243a2744567a2f3b70fe64f5d7dd5c592dc95636a3271c3e0c7a49e5f1a91","observation_id":"10b05c39-daa2-4ce8-aba0-af038accb5cf","resolution":{"observed_at":"2026-08-10T16:40:59.974884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.953013Z","title":"Neural adaptive video streaming with pensieve","venue":null,"work_id":"5323d875-76fd-4fff-b5f4-6534b0f1d67b","year":2017},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.863316Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:8d3e1329608aea13e86cb94cef179f7b522df6c6596173b237f6ded751321364","observation_id":"715cca1e-79e6-4341-acac-0269429a0c07","resolution":{"observed_at":"2026-08-10T16:40:59.958337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.936025Z","title":"A queuing theory model for fog computing","venue":null,"work_id":"1ff4cfcc-d182-4692-87fb-a41bdf5d4370","year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.868898Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:00f0a7c894c294709f5465cf09792271aaa8613203ce3dd3babf1ebe584a8567","observation_id":"baea1345-ff25-4361-b65d-eb11689dab01","resolution":{"observed_at":"2026-08-10T16:40:59.941046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.919931Z","title":"Power allocation in multi-user cellular networks: Deep reinforcement learning approaches","venue":null,"work_id":"4c543e19-200b-4545-973c-b3072b7b8f5c","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.874203Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:84e9da41c44b3fe42e7d4db00f0976d68b7a5d4f5bb80e3850f8344b1c8f044f","observation_id":"7478aea4-ebb0-40a2-892e-6cf35439073a","resolution":{"observed_at":"2026-08-10T16:40:59.924972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02074","last_updated":"2019-12-04T16:06:10Z","snapshot_observed_at":"2026-08-05T10:28:32.357062Z","submitted_at":"2019-12-04T16:06:10Z","title":"AlgaeDICE: Policy Gradient from Arbitrary Experience","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02074","snapshot_observed_at":"2026-08-10T16:40:58.879382Z","title":"Al- gaedice: Policy gradient from arbitrary experience","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.879382Z"},"links":{"cited_paper":"/paper/1912.02074","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:6df14aa837e283c18ddb6762d2c046bec274f9415ef38b16702c674e763bb8c4","observation_id":"7f6f0cae-dab6-4f0e-a775-87e0cb76c850","resolution":{"observed_at":"2026-08-10T16:40:58.879382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09359","last_updated":"2021-04-24T22:39:30Z","snapshot_observed_at":"2026-07-06T09:29:45.475911Z","submitted_at":"2020-06-16T17:54:41Z","title":"AWAC: Accelerating Online Reinforcement Learning with Offline Datasets","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09359","snapshot_observed_at":"2026-08-10T16:40:58.885002Z","title":"Awac: Accelerating online reinforcement learning with offline datasets","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.885002Z"},"links":{"cited_paper":"/paper/2006.09359","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:662dcfd15de3ec1f2c38d7868c4a0f9741c90e96bd18eecec80bcffa52ce51cc","observation_id":"e0a0a6ad-a5f4-4081-8b7c-3ee819c78dc8","resolution":{"observed_at":"2026-08-10T16:40:58.885002Z","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":"2026-08-10T16:40:59.904588Z","title":"Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks","venue":null,"work_id":"3ae03fc9-2c6b-4905-945d-ce8cf86dac52","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.898176Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:03f080225b0e698c9eeb29aaa19eda0506dfbb74101591305a73be82366fa5fa","observation_id":"c85ed8bd-6c63-436e-812e-c01670288bca","resolution":{"observed_at":"2026-08-10T16:40:59.909548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.888517Z","title":"Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification","venue":null,"work_id":"55cd0a74-286d-4d28-ac2f-77920cbce4d8","year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.904171Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:9fc398fab09222ed8987806d142f02c10a9de23aa15d21979c7c50175a8f0341","observation_id":"6287a856-fb44-4764-a4d6-d41d3da53147","resolution":{"observed_at":"2026-08-10T16:40:59.894039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.870518Z","title":"Energy-optimal scheduling of mobile cloud computing based on a modified lyapunov optimization method","venue":null,"work_id":"4550b118-352d-408e-a6be-2052cd12d711","year":2017},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.910065Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:751c15e2de033e001c84ec5d9d7ad4f5087a1f0a13927e934047ef1ed2238c35","observation_id":"d8845541-7245-4451-a686-35401b5bf420","resolution":{"observed_at":"2026-08-10T16:40:59.876244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.853459Z","title":"Energy-optimal scheduling of mobile cloud computing based on a modified lyapunov optimization method","venue":null,"work_id":"2a1df4aa-6e50-4fce-9000-7893b157aaae","year":2018},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.915583Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:5f9e7eeabb903b745e961d7bf71a409488b01b88cb13a08a2c6c17939dfff561","observation_id":"a902bbbb-a3b9-416e-bfd3-8fb425cb0310","resolution":{"observed_at":"2026-08-10T16:40:59.858448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09329","last_updated":"2024-10-28T23:33:19Z","snapshot_observed_at":"2026-08-10T00:32:45.181466Z","submitted_at":"2024-06-13T17:07:49Z","title":"Is Value Learning Really the Main Bottleneck in Offline RL?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09329","snapshot_observed_at":"2026-08-10T16:40:58.920844Z","title":"Is value learning really the main bottleneck in offline rl? arXiv preprint arXiv:2406.09329 , 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.920844Z"},"links":{"cited_paper":"/paper/2406.09329","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:4de8be3a259034c27de9ad3728c857216e950fae78e6274ff55eff2805b8f899","observation_id":"ee382915-9493-4040-b19d-5e364864e4ee","resolution":{"observed_at":"2026-08-10T16:40:58.920844Z","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":"2026-08-10T16:40:59.837773Z","title":"Online convex optimization for caching networks","venue":null,"work_id":"0e67625f-2b3a-4423-9676-1361938ebadd","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.926481Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:85bd4d0efb92d859dfb3ad079799cfcb6cb732e4c0d68cb7edcd0001090d8b25","observation_id":"75f07e48-7d15-4081-a532-2a1f6a126b8e","resolution":{"observed_at":"2026-08-10T16:40:59.842918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00177","last_updated":"2019-10-07T20:23:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-10-01T02:23:38Z","title":"Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00177","snapshot_observed_at":"2026-08-10T16:40:58.931781Z","title":"Advantage-weighted regres- sion: Simple and scalable off-policy reinforcement learning","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.931781Z"},"links":{"cited_paper":"/paper/1910.00177","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:a6ff456c2285544752868f8a7daefc910d66aad4a35656d83695d3bf8b1b1200","observation_id":"27169377-e99f-4a47-b7e5-63db85401bc8","resolution":{"observed_at":"2026-08-10T16:40:58.931781Z","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":"2026-08-10T16:40:59.822097Z","title":"Youtube live and twitch: a tour of user-generated live streaming systems","venue":null,"work_id":"e0b9bd88-00d8-4ee1-af68-cece13f2c5f5","year":2015},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.937285Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:249735b9063b4bf19880b36c381058f6001ce21a9668490bc643414b324ad644","observation_id":"3477d877-8561-4ea9-83fd-54a1b6bef99a","resolution":{"observed_at":"2026-08-10T16:40:59.827064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.806166Z","title":"Learn- ing to solve np-complete problems: A graph neural network for decision tsp","venue":null,"work_id":"0aca4637-fb2e-4cb2-b146-a5f8ffdf7fa4","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.942687Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:faa26c99b5b0d6428b7df4e4b0629c40f1ece9a2b5eaa0308a02200c51f9a115","observation_id":"60707021-027c-4a18-9d48-821e35b4b6f8","resolution":{"observed_at":"2026-08-10T16:40:59.811515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.789010Z","title":"Class-balancing diffusion models","venue":null,"work_id":"593532e0-caee-4ddd-a4db-d89102d70f5f","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.948157Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:22b3393a191d2da50984d0c615b22d72deae15febc1e46097a7754df74e4f702","observation_id":"3b828ae6-80a8-404e-a356-1c6b08dd9732","resolution":{"observed_at":"2026-08-10T16:40:59.794331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:58.953805Z","title":"Random features for large-scale kernel machines","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.953805Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:fbbf40e568c0abf292f4aa2aae408fbe0d829308eb2ffca8e55dbac6fc872123","observation_id":"d2346cf3-6fde-4b68-b25e-fec4eb91a6dc","resolution":{"observed_at":"2026-08-10T16:40:58.953805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-08-08T18:23:31.977872Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-10T16:40:58.958862Z","title":"Ramachandran, B","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.958862Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:593cab51d02c80fcf0c1139a6576e9d2ab647445b42058f3f0a1ea098a934de3","observation_id":"3079ee55-f0ac-45b6-9904-474566542cbc","resolution":{"observed_at":"2026-08-10T16:40:58.958862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-10T16:40:58.964581Z","title":"Hierarchical text-conditional image generation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.964581Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:4fef24beb7b41e5fe25579a973b95f43ecee5d6b311024e86c5df48d6c41d691","observation_id":"c5f76dce-ddc7-4719-a28b-8e2922728989","resolution":{"observed_at":"2026-08-10T16:40:58.964581Z","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":"2026-08-10T16:40:59.762234Z","title":"Offline reinforcement learning as anti-exploration","venue":null,"work_id":"4d8a1e51-e44e-4f7c-b1fa-1795ecd636ac","year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.970169Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:f94e1dbc6ae476047edf4a4efb88aa3d159a9125ac1f2321e3fefa160b643644","observation_id":"f83ec46b-f3ff-4b33-9283-d55b0603c44e","resolution":{"observed_at":"2026-08-10T16:40:59.767787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.746493Z","title":"Simple near-optimal scheduling for the m/g/1","venue":null,"work_id":"2fbc3426-d981-46fb-8339-333ff46124c1","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.975630Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:bbd576a078b8ff23c922b2d14d78b64dba4f7a66ca9c4b40ad4297466a2d5aea","observation_id":"4ca6f296-4bbf-4d52-8db4-2915e48c5f4b","resolution":{"observed_at":"2026-08-10T16:40:59.751498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08396","last_updated":"2020-06-17T10:12:44Z","snapshot_observed_at":"2026-08-11T03:30:21.229658Z","submitted_at":"2020-02-19T19:21:08Z","title":"Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08396","snapshot_observed_at":"2026-08-10T16:40:58.980888Z","title":"Keep doing what worked: Behavioral modelling priors for offline reinforcement learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.980888Z"},"links":{"cited_paper":"/paper/2002.08396","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:6c8e3401e982d20b00f34ba315e0eba933185380642e9eeb654a46d48bae75ea","observation_id":"b3654b1a-bf95-4193-866f-8d1f87e5cc99","resolution":{"observed_at":"2026-08-10T16:40:58.980888Z","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":"2026-08-10T16:40:59.730532Z","title":null,"venue":null,"work_id":"ac1cdfff-0033-4fae-a862-58a5646a4c55","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.986341Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:7209a3be6380ac3a626127cefcfdf4acfecf68c66ac0d85081e2641a35eb21b4","observation_id":"155b2081-36ce-4ed5-9bae-03935ffcafd0","resolution":{"observed_at":"2026-08-10T16:40:59.735764Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:58.991479Z","title":"Deep unsuper- vised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.991479Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:da4dd23e59a4f43cd8a1fe9aa2fe270050ad6d92977cd100e8fe89dff43691ec","observation_id":"6a4740bd-2ef7-4c2c-8718-8cd00c051684","resolution":{"observed_at":"2026-08-10T16:40:58.991479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-10T16:40:58.997183Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:58.997183Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:064c029e7b320840a1f7e270c4ba7b043e1be30b39eb255f0b81c8286cae89a7","observation_id":"31a4bea0-2260-4282-9c83-976d571a5418","resolution":{"observed_at":"2026-08-10T16:40:58.997183Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:40:59.002715Z","title":"Generative modeling by estimating gradients of the data distribution","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.002715Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:5edb66c341909cfc2ee3b9202a1a704981398635a6587c649a22e10e48afa17b","observation_id":"2ba50498-f344-4bc4-ad93-00a77705a96b","resolution":{"observed_at":"2026-08-10T16:40:59.002715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-10T16:40:59.008316Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.008316Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:1785137305e4b0ff480c886dff54d10d47bc1f15c13fd4a3a596b03444fcc908","observation_id":"d679a13a-7d1a-4ec2-853e-f057dfc106c2","resolution":{"observed_at":"2026-08-10T16:40:59.008316Z","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":"2026-08-10T16:40:59.690010Z","title":"Batch learning from logged bandit feed- back through counterfactual risk minimization","venue":null,"work_id":"36c44b6d-09e2-4a09-951a-aff6e1c5bb78","year":2015},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.013751Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:f2a8d71d966498ec94a5d3db672c63f5add6fad25d45475aae58d74de77f5f2f","observation_id":"a472f1b7-239b-4f79-9aff-ae00314396b7","resolution":{"observed_at":"2026-08-10T16:40:59.695269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.672993Z","title":"Minimizing age of information with power constraints: Multi-user opportunistic scheduling in multi-state time-varying channels","venue":null,"work_id":"9b9e90bc-c545-4df5-9378-fe4460d4e413","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.019238Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:2dc62d306f28291bf925cdf182c6b2e8517f929034529e9a6769592dfcb29512","observation_id":"97bd790a-496e-49a8-939c-75451e36270d","resolution":{"observed_at":"2026-08-10T16:40:59.678183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.657053Z","title":"Taotao, X","venue":null,"work_id":"a9bfbbd4-b011-4736-b223-19b1d8082242","year":2021},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.024473Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:f86cbd3a7cab2d4e4a0b0d6bc4fe555220d20080bb9de4d444e7484e8d2a635b","observation_id":"531f037a-cb62-40f1-a0fc-d8b5e5acf91e","resolution":{"observed_at":"2026-08-10T16:40:59.661960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.641016Z","title":"Learning combinatorial optimization on graphs: A survey with applications to networking.IEEE Access, 8:120388–120416, 2020","venue":null,"work_id":"13ee9712-955e-48ca-a80d-568215c4482d","year":2020},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.029699Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:16a9a145adcee604262dbc5f6c8f4ce331622cdcdc859e1b5c12c3bfb550c4f8","observation_id":"6dfcf16c-fcf9-4b8b-bd50-75a27378687b","resolution":{"observed_at":"2026-08-10T16:40:59.646262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.623504Z","title":"Scheduling real-time wireless traffic: A network-aided offline reinforcement learning approach","venue":null,"work_id":"c4179713-e6ca-4f37-84b9-720fc93e487e","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.034952Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:b404c4987437f3440d8cba991cc4b54d4fbdbe8595c1e0be73afc87331ad82e2","observation_id":"896443db-3b0a-4039-a882-2aee1f53b849","resolution":{"observed_at":"2026-08-10T16:40:59.629238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.605953Z","title":"Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning","venue":null,"work_id":"fee48ed7-a70c-430b-9a83-a62346a69a8f","year":2023},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.040575Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:1b068e5985ace3d452da659a096343613160da60578054196cc2c0e3aa64a183","observation_id":"b679d4b7-349c-4b7c-ab55-bb7b16450d45","resolution":{"observed_at":"2026-08-10T16:40:59.611236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.06193","last_updated":"2023-08-25T19:39:32Z","snapshot_observed_at":"2026-07-06T13:41:14.687081Z","submitted_at":"2022-08-12T09:54:11Z","title":"Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.06193","snapshot_observed_at":"2026-08-10T16:40:59.046141Z","title":"Diffusion policies as an expressive policy class for offline reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.046141Z"},"links":{"cited_paper":"/paper/2208.06193","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:d92497b2d9bdf4415aacddabc112ef3b0da205b8b6f52a921fe5163e1f51b2a6","observation_id":"aad60dac-a116-4642-b486-1529543fe211","resolution":{"observed_at":"2026-08-10T16:40:59.046141Z","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":"2026-08-10T16:40:59.589571Z","title":"Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation","venue":null,"work_id":"61bd3c5e-14ef-41eb-85c3-530a93b842ff","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.051730Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:d4bf8e68f2bb327d3387f735bf0a3df802277f45a1a7ba5948a7638b7bc90b3d","observation_id":"337eb5b7-8272-46fe-b0b7-2dc0357b8d8c","resolution":{"observed_at":"2026-08-10T16:40:59.595003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11361","last_updated":"2019-11-26T06:11:34Z","snapshot_observed_at":"2026-07-06T08:39:58.361914Z","submitted_at":"2019-11-26T06:11:34Z","title":"Behavior Regularized Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11361","snapshot_observed_at":"2026-08-10T16:40:59.057244Z","title":"Behavior regularized offline reinforcement learn- ing","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.057244Z"},"links":{"cited_paper":"/paper/1911.11361","citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:777c446ab9566c4a9687b00d24de62e24e70005e9c3de61b0bfc3b245d69c5bb","observation_id":"d3af3dad-5843-4e3a-8d2c-39a438ec8b83","resolution":{"observed_at":"2026-08-10T16:40:59.057244Z","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":"2026-08-10T16:40:59.573200Z","title":"Understanding instant messaging traffic characteristics","venue":null,"work_id":"78439fe6-4df7-47b3-a113-1c42f0a44670","year":2007},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.062825Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:505f7172c12dc27d2a87ef2072dd513d3b6647bce8992d424d654189b87477ee","observation_id":"02fe5a04-1f6a-4738-82cd-92d1976399d8","resolution":{"observed_at":"2026-08-10T16:40:59.578295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.556944Z","title":"Offline reinforcement learning for wireless network optimization with mixture datasets","venue":null,"work_id":"09061dce-cb85-487f-ba66-2831d21c6caa","year":2024},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.068106Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:94cfc52de3f5c5f7ff9615ab907c9355ebb1db9c03e7c49e52de3cd6763d4b5e","observation_id":"1f6a8f42-2456-482d-9ba8-8fc1436aa01c","resolution":{"observed_at":"2026-08-10T16:40:59.561972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T16:40:59.539268Z","title":"Reles: A neural adaptive multipath scheduler based on deep reinforcement learning","venue":null,"work_id":"3616b47e-082a-44f2-88e0-6e1a207faa47","year":2019},"citing_paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-10T16:40:59.073705Z"},"links":{"citing_paper":"/paper/2501.12942"},"observation_digest":"sha256:b10068bfb30f59f66088ecfd33805459897db9666587a7d8da329e2cbdaada85","observation_id":"57dc52da-0a73-4d4d-8da5-0c082b2265f2","resolution":{"observed_at":"2026-08-10T16:40:59.545078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.12942","last_updated":"2026-06-10T06:14:49Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-11T01:48:30.136061Z","submitted_at":"2025-01-22T15:13:21Z","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling"},"reference_resolution":{"displayed":87,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":39,"verified_exact":1,"verified_fuzzy":47},"total_outbound_references":87},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 1 inbound Pith citation observation for arXiv:2501.12942."}