{"as_of":"2026-08-07T22:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f76100918811d96f519f880c1b389364c3f5aee99bda322b525c0609b41880e","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:45:56.582881Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T21:02:04.162159Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T20:53:58.379898Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.12910","snapshot_observed_at":"2026-08-05T21:02:04.162159Z","title":"Energy-Efficient RSMA- enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.09561","last_updated":"2025-08-13T07:29:40Z","snapshot_observed_at":"2026-08-07T09:03:01.626297Z","submitted_at":"2025-08-13T07:29:40Z","title":"Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T21:02:04.162159Z"},"links":{"cited_paper":"/paper/2507.12910","citing_paper":"/paper/2508.09561"},"observation_digest":"sha256:9df1bb6a8f6c663ae6c72d3e573c767f0868b933269f215a5cac9b6b5670ea73","observation_id":"42b7274b-1266-4892-bfe4-0f3599d64cad","resolution":{"observed_at":"2026-08-05T21:02:04.162159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2507.12910","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12910","snapshot_observed_at":"2026-06-29T20:53:58.379898Z","title":"Energy-efficient rsma- enabled low-altitude mec optimization via generative ai-enhanced deep reinforcement learning,","venue":null,"work_id":"0f9bba54-90d1-4a4b-8ad6-9eddc4e6984e","year":2025},"citing_paper":{"arxiv_id":"2605.25531","last_updated":"2026-05-25T07:37:39Z","snapshot_observed_at":"2026-08-05T10:02:09.642060Z","submitted_at":"2026-05-25T07:37:39Z","title":"From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-29T20:49:07.030872Z"},"links":{"cited_paper":"/paper/2507.12910","citing_paper":"/paper/2605.25531"},"observation_digest":"sha256:dcf2b8d47b5367d16a1537340f5804593797a9caa50434ca9c2e3aed36d7a3a7","observation_id":"7ad879fd-a3f8-4982-8454-2c75447d43a6","resolution":{"observed_at":"2026-06-29T20:53:58.381394Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.12910/citation-record","integrity":"/paper/2507.12910/integrity","json":"/paper/2507.12910/citation-record.json","paper":"/paper/2507.12910"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.923198Z","title":"6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication,","venue":null,"work_id":"0ee1f43a-0515-4ff7-ab6e-90b050088142","year":2019},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:51.786208Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:16d6b6be6f1ddbb2e192764ac455708c6e07e086899fb490ab10e1af20f063f1","observation_id":"73d51c40-3562-4dc5-8154-d1cf604cb7f5","resolution":{"observed_at":"2026-08-06T16:46:02.991157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.795516Z","title":"Survey on Multi-Access Edge Computing for Internet of Things Realization,","venue":null,"work_id":"1c8703fa-b950-4f91-abbf-8ebeb27735d8","year":2018},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:51.888418Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:29fd112998c71f0c51a73ee2553374e9ae9e17c7c5252fe32a554f82bc80b7d4","observation_id":"53da8586-bc1c-4039-b182-f000c383d3f7","resolution":{"observed_at":"2026-08-06T16:46:02.845457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.670916Z","title":"Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges,","venue":null,"work_id":"469e8e9a-a2e4-49d1-abff-832f0c494415","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.038139Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:2a7cd72b97c7573e09c9c5870519598d0967d20b5c77858c042adfa636a2a37c","observation_id":"79baab85-e4aa-48f5-858b-ac7a4cb6b18b","resolution":{"observed_at":"2026-08-06T16:46:02.724990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.532642Z","title":"Integrated Sensing and Communication for Low Alti- tude Economy: Opportunities and Challenges,","venue":null,"work_id":"42b0db96-b404-4623-99e7-f89fa2b46561","year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.153207Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:34e6e1e4fc591fb23c598fc36c88d0c1fa29767f03efa1824f556309a2f77b86","observation_id":"dcd3b3d7-d941-42a5-aa47-715583730b23","resolution":{"observed_at":"2026-08-06T16:46:02.603835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18118","last_updated":"2025-07-26T07:22:48Z","snapshot_observed_at":"2026-08-07T17:50:23.178640Z","submitted_at":"2025-02-25T11:42:44Z","title":"Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18118","snapshot_observed_at":"2026-08-06T16:45:52.320309Z","title":"Generative AI-enabled Wireless Communi- cations for Robust Low-altitude Economy Networking,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.320309Z"},"links":{"cited_paper":"/paper/2502.18118","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:4283fb1073b5049f8aab21428be3c4daca5b82f179b6fd25be6564ea17bc9147","observation_id":"35626717-e9c6-4bd1-a128-c2fb51b0bca9","resolution":{"observed_at":"2026-08-06T16:45:52.320309Z","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":"2026-08-06T16:46:02.378076Z","title":"Mobile Edge Computing via a UA V- Mounted Cloudlet: Optimization of Bit Allocation and Path Planning,","venue":null,"work_id":"e960ea93-20dc-416c-86e8-1b23c653ae29","year":2018},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.445193Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:c7eecf82f06f11e8f0c1fee28d11709ce8485807a7f24720ca137da62beed17c","observation_id":"79631819-09da-4a05-8933-76d8891a02c3","resolution":{"observed_at":"2026-08-06T16:46:02.459202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.244932Z","title":"Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends,","venue":null,"work_id":"c8894417-2bbd-4347-9c0b-4a3196fc4cfa","year":2022},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.573302Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:47550cc5040eef97529b6be5c7bd2be448bb4ece7a48036641cbdfbfb9434ae0","observation_id":"be72fa46-74d4-4173-9408-3b6423c97369","resolution":{"observed_at":"2026-08-06T16:46:02.308182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:02.100423Z","title":"Resource Allocation and User Pairing for Rate Splitting Multiple Access Based Wireless Networked Control Systems,","venue":null,"work_id":"44cf912b-a2fb-47f8-b50b-b0314bb4cca8","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.709665Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:5564f5e577f361f953fc466f2875dc56cc93efcb5800f1c6b85840bdc6d6f47b","observation_id":"32b686a1-94ca-4b32-8377-67bca41116ae","resolution":{"observed_at":"2026-08-06T16:46:02.162791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:01.958138Z","title":"Large-Scale Rate-Splitting Multiple Access in Uplink UA V Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel,","venue":null,"work_id":"1627d274-ff62-4253-8e87-8071e3825220","year":2023},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.852080Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:c1c18040628ec36eb894bdd3bcf980a775d46efd796ad094a53e9a8a8660191a","observation_id":"235cb2b2-3eac-4b52-bd49-618671eb43b9","resolution":{"observed_at":"2026-08-06T16:46:02.006794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:01.788995Z","title":"Multi- Objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-Enabled Deep Reinforcement Learning,","venue":null,"work_id":"6645b680-3304-4e5d-bc85-f779f7d3cf3e","year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:52.959970Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:790f3e9889cfa432918972c50e91f00d81f20f97c2fc34e224842de2cef355c3","observation_id":"26789b9f-37a9-47ef-bf57-321468d6a7ef","resolution":{"observed_at":"2026-08-06T16:46:01.865408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:01.663808Z","title":"DRL-Driven Joint Task Offloading and Resource Alloca- tion for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks,","venue":null,"work_id":"ba80e780-6d2f-4ef2-a2a5-57bc0717ff29","year":2023},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.117326Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:ac0e393de03765ffb5b1d86a03b4a60c0c613429235c9bc0bb906486fdcc6414","observation_id":"beebcbe2-0474-47a3-9093-4663c2900faf","resolution":{"observed_at":"2026-08-06T16:46:01.713795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:53.258128Z","title":"ReaCritic: Large Reasoning Transformer-based DRL Critic-model Scaling For Heterogeneous Networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.258128Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:efc8e9670d4e5d1108b37c24dc1378ad372732e8b7ecb82145a1ba68a165b617","observation_id":"9a1a7827-e4df-4b48-b65c-f07b18039931","resolution":{"observed_at":"2026-08-06T16:45:53.258128Z","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":"2026-08-06T16:46:01.532883Z","title":"Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,","venue":null,"work_id":"acb75306-2f12-40ff-900b-52d72aa6fca0","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.380773Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:344113d99a35ad58fa1d2291369523f1e207defef5c53884f706987576b9ec2f","observation_id":"17e1a6a9-0c36-4fb2-a5b3-cf923254d3c9","resolution":{"observed_at":"2026-08-06T16:46:01.590797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04137","last_updated":"2024-10-29T13:01:26Z","snapshot_observed_at":"2026-08-06T11:09:42.942896Z","submitted_at":"2024-10-29T13:01:26Z","title":"Generative AI Enabled Matching for 6G Multiple Access","version":1},"cited_work":{"arxiv_id":"2411.04137","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.04137","snapshot_observed_at":"2026-08-06T16:45:56.718868Z","title":"Generative AI Enabled Matching for 6G Multiple Access","venue":"cs.NI","work_id":"56697198-97c3-462e-ac94-737e689661e3","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.537181Z"},"links":{"cited_paper":"/paper/2411.04137","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:ed6a4734723469a98788aafc131cc4fdf4446f52d752ca4f699312a9ec9ee46d","observation_id":"8f659c0d-7877-4224-8c8c-ebb8d0fc3432","resolution":{"observed_at":"2026-08-06T16:45:56.783714Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:01.380971Z","title":"Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective,","venue":null,"work_id":"dd397203-9c8a-4e21-8f88-02d83bccac2e","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.679469Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:4ba207a04a3099a1879fed6b531e697386395958ada6f652c4253076d0ff2f97","observation_id":"753ee779-bee7-40e7-8b93-b99b8db89c0b","resolution":{"observed_at":"2026-08-06T16:46:01.466018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07433","last_updated":"2025-04-22T15:52:17Z","snapshot_observed_at":"2026-08-07T17:16:27.541697Z","submitted_at":"2025-03-10T15:15:29Z","title":"DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07433","snapshot_observed_at":"2026-08-06T16:45:53.799857Z","title":"DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.799857Z"},"links":{"cited_paper":"/paper/2503.07433","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:11a0533c5a5085c67dc7dbbb86f4d01f71ea2cb314216e2b7715ff0c42d4822f","observation_id":"b2ddd169-584f-464a-b207-132d80428b9c","resolution":{"observed_at":"2026-08-06T16:45:53.799857Z","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":"2026-08-06T16:46:01.209692Z","title":"Dynamic Offloading and Trajectory Control for UA V-Enabled Mobile Edge Computing System With Energy Harvesting Devices,","venue":null,"work_id":"0bae9754-8e8e-45a8-adfd-c40984f4be3a","year":2022},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:53.924393Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:800923d53535651e3e3c8cdc0cdc9ba2fb63caba6e406ea702711f6d4a1283bf","observation_id":"17b3f9dd-d755-4c0d-adb5-c6b1c5c758d4","resolution":{"observed_at":"2026-08-06T16:46:01.297647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:01.066541Z","title":"Computation Efficiency Maximization and QoE-Provisioning in UA V-Enabled MEC Communication Systems,","venue":null,"work_id":"2935962a-7357-4656-a7fa-e9a0da44adc7","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.102189Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:a1907f423c22769f04beecf48e542c8429af001310dba7481e886242967b27ab","observation_id":"acf81814-e4c6-4206-b6ec-9a26ce5ac04d","resolution":{"observed_at":"2026-08-06T16:46:01.155535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.916354Z","title":"Resource Allocation and Trajectory Design for MISO UA V-Assisted MEC Networks,","venue":null,"work_id":"51c1c0a3-925a-474d-9a32-1019e3fb51a8","year":2022},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.229448Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:8c913f4d61dfa80da412757f5e6928242b573266383b7da513cfab9f0118e78c","observation_id":"b5f50789-a8c1-433c-969a-a3719ed4fe42","resolution":{"observed_at":"2026-08-06T16:46:00.984440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.733910Z","title":"Bi-Objective Ant Colony Optimization for Trajectory Planning and Task Offloading in UA V- Assisted MEC Systems,","venue":null,"work_id":"9deb3a05-3c64-489b-9efd-fa567180db76","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.316712Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:a1a8faf9b1800175bd46588000f5a4f41cd4f07e3436e88aa31e7a2547601ee1","observation_id":"57d1269c-0413-4797-8d18-518d2ce885a0","resolution":{"observed_at":"2026-08-06T16:46:00.822368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.621443Z","title":"Optimal Trajectory and Resource Allocation for RSMA-UA V Assisted IoT Communications,","venue":null,"work_id":"76c3d420-670c-4eaf-9da0-1a4a1dc759bf","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.433604Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:783ff15a274424be679f103473ad21b9f12b2f093987b7c06c9f4707747a9ba2","observation_id":"f77359ce-d3c3-4c67-a616-dd0087f942d2","resolution":{"observed_at":"2026-08-06T16:46:00.686137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.456569Z","title":"On the Physical Layer Security of the Cooperative Rate- Splitting-Aided Downlink in UA V Networks,","venue":null,"work_id":"a0e7d3ca-be65-4e68-9593-801bf6ce1335","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.536752Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:e4f4d8191b6435c741d5ba623e6dd2a004bf265f491fc3b19ac7454232898413","observation_id":"7b8d5b98-bb5a-4b1b-a945-f22702b29630","resolution":{"observed_at":"2026-08-06T16:46:00.536395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.281039Z","title":"Rate Splitting on Mobile Edge Computing for UA V-Aided IoT Systems,","venue":null,"work_id":"1c66c716-6ffc-41e0-bea0-89fce98351a3","year":2020},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.584008Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:6313e604b102acc6e63d6a8e937aa7f01c0e13043d0cd164db2fe5534dc3fff0","observation_id":"3488245f-9aec-4f0f-8114-675949685714","resolution":{"observed_at":"2026-08-06T16:46:00.345290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:46:00.148161Z","title":"PRU Group Allocation and Dynamic Rate-splitting Design for Power Minimization in IRS- assisted UA V MEC Systems With RSMA,","venue":null,"work_id":"0c49a848-158b-432e-b108-ec20fc29bd20","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.662922Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:2fe0fd3c1bb6901e255c8a74d71f168ad88875fbe3d75546b88eca058e4c582c","observation_id":"98c4fea1-4fa5-4c83-9bcd-ba1658ece14b","resolution":{"observed_at":"2026-08-06T16:46:00.198836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.976122Z","title":null,"venue":null,"work_id":"7ba83f25-3b54-4984-8bc6-95ebcf8e1915","year":2023},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.769193Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:2d48755d693911cd38677da5d813d73ac42d154d320ca57ad992b2507ab694c5","observation_id":"a9785446-1b19-439d-a2c9-b5da97d0e263","resolution":{"observed_at":"2026-08-06T16:46:00.066632Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.803434Z","title":"Deep Reinforcement Learning Based Resource Allocation in Multi- UA V-Aided MEC Networks,","venue":null,"work_id":"cc3c5bcd-d3b7-49b9-9a52-b3fe5c9476d7","year":2023},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.862180Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:48df6f645b3744cec452306b0c7ae30f07fe0508e4fe70a40a6d0eee5b9d2861","observation_id":"9e351313-b50a-4d5b-a30f-255439b3d426","resolution":{"observed_at":"2026-08-06T16:45:59.878741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.648584Z","title":"Optimizing Resource Allocation for Multi-Modal Semantic Communication in Mobile AIGC Networks: A Diffusion- Based Game Approach,","venue":null,"work_id":"3a1b60da-7717-441b-a68f-c1d4aafd9d1b","year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:54.967729Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:e2e65b7e1ec07d660ee6e6d4f9c10cb04827f1c38424579a1283b55cd628a1e4","observation_id":"2fe031e6-1f97-47a3-bcac-0882cc42ffa9","resolution":{"observed_at":"2026-08-06T16:45:59.731576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15571","last_updated":"2025-05-21T14:25:31Z","snapshot_observed_at":"2026-08-07T15:12:33.806556Z","submitted_at":"2025-05-21T14:25:31Z","title":"Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15571","snapshot_observed_at":"2026-08-06T16:45:55.094209Z","title":"Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.094209Z"},"links":{"cited_paper":"/paper/2505.15571","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:6f65b40f1ae9ba45f0a396b5b02833932fbfbc83231210754c6642a85ba1d9c1","observation_id":"ff859b2f-dd4f-43e6-bbd6-184f411b9b5a","resolution":{"observed_at":"2026-08-06T16:45:55.094209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07211","last_updated":"2025-02-11T03:09:45Z","snapshot_observed_at":"2026-07-06T20:34:29.976207Z","submitted_at":"2025-02-11T03:09:45Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07211","snapshot_observed_at":"2026-08-06T16:45:55.178372Z","title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.178372Z"},"links":{"cited_paper":"/paper/2502.07211","citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:f5c704c694c5ecd654fb908db3bb191082f829e69b010f0406711ab029a50f5d","observation_id":"bc443023-c0cc-4966-b735-b6a143f55e99","resolution":{"observed_at":"2026-08-06T16:45:55.178372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.450282Z","title":"Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach,","venue":null,"work_id":"5a966425-6017-46da-96a7-0faaa40156af","year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.270117Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:11c2ab2922a7e0675bf34fedaeecd60a60fd68f7712a623b672f695d2737cec8","observation_id":"2c9431fd-0e6a-4256-b5c5-72d1207c2d8e","resolution":{"observed_at":"2026-08-06T16:45:59.557587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.279349Z","title":"Enhanced Secure Beamforming for IRS-Assisted IoT Communication Using a Generative- Diffusion-Model-Enabled Optimization Approach,","venue":null,"work_id":"c2636959-2234-4f62-b6b6-7536fc74fad4","year":2025},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.367780Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:49153acc257be70c440960fda7a456a56d6e347c0c52b444e81959dda1ed11f5","observation_id":"b1c7f76c-8046-469c-8c62-601ce31e4265","resolution":{"observed_at":"2026-08-06T16:45:59.339385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:59.092165Z","title":"A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks,","venue":null,"work_id":"98302d96-9146-459d-a057-3a538ea236f1","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.484757Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:dd8e24b98300b32c5f8178d744724e31dc4a64242604a47c207832428e631518","observation_id":"95869b6a-7568-4f25-a8a2-2b18874f1595","resolution":{"observed_at":"2026-08-06T16:45:59.155012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:58.908138Z","title":"Joint Trajectory-Task- Cache Optimization in UA V-Enabled Mobile Edge Networks for Cyber- Physical System,","venue":null,"work_id":"4ff68322-9b3d-4d17-b35b-983bd7f07d6e","year":2019},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.566628Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:7d14e80ee8480a961bee0c2c908447768951c28c1f3234f6a48f909cd5ccafd2","observation_id":"edcdc133-1cb8-47c0-b77c-a51be531859e","resolution":{"observed_at":"2026-08-06T16:45:59.004587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:58.751180Z","title":"Optimal LAP Altitude for Maximum Coverage,","venue":null,"work_id":"91cb699a-43d8-4b72-87f3-52e34e6f14da","year":2014},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.620462Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:c17700517be1fcc6f91f532c315810198776bcf43d7e86c1940e44f62a8e11b7","observation_id":"f51f6b92-7ff2-4ce2-b0be-ca2e43cc61a4","resolution":{"observed_at":"2026-08-06T16:45:58.836646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:58.578678Z","title":"Energy-Efficient Resource Allocation in UA V Based MEC System for IoT Devices,","venue":null,"work_id":"29c50c98-422c-4ec0-9e8d-95b739a1b852","year":2018},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.713742Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:68e44d96ac03e81c9367ed877f003362abc25299fe1a675da130092fb00a6ab5","observation_id":"b62457c9-8122-407b-b5d8-df2e9e03ec5e","resolution":{"observed_at":"2026-08-06T16:45:58.670610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:58.364410Z","title":"Sum- Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication,","venue":null,"work_id":"1212e148-0fb5-4f56-a9a1-7ccc5d28cc29","year":2022},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.800905Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:7e3e74b76737a0fcff3dd24154fe05cf8f20760c4c9d4e7bdbe7ff36c59caf6d","observation_id":"7cf56f6f-06b3-4018-a530-4486a0fd9275","resolution":{"observed_at":"2026-08-06T16:45:58.459855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:58.177103Z","title":"Power Efficient IRS-Assisted NOMA,","venue":null,"work_id":"efb6addc-8b01-4b0d-9adb-a99e05b44482","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:55.924015Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:58a41fcffef3ca2456f256a961118aac0fe89976280076ea5f4a307b823921ee","observation_id":"1f30cbd2-7652-4649-b358-79a779584326","resolution":{"observed_at":"2026-08-06T16:45:58.268911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.930634Z","title":"Sum Rate Maximization for IRS-Assisted Uplink NOMA,","venue":null,"work_id":"91d757bd-d6e1-4589-bdbb-25357716960a","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.023268Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:d7c7ee6039f28bc2e4637c143f87ee2815bbc15dc8c36f1ce2539dec6ff4cf19","observation_id":"60ba1df3-5cdd-441c-9d75-37644b120854","resolution":{"observed_at":"2026-08-06T16:45:58.051835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.754594Z","title":"Optimal and Sub-Optimal Uplink NOMA: Joint User Grouping, Decoding Order, and Power Control,","venue":null,"work_id":"7308a95c-750a-456d-92f5-96e884e51ea6","year":2020},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.092162Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:afe4f9dedc9920525bdc301dde2465d05fc5c51011d63df3abc489fcd34b67e4","observation_id":"168eeaae-01f7-4a21-80d9-469fe3f29ae8","resolution":{"observed_at":"2026-08-06T16:45:57.833587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.591788Z","title":"Deep Reinforcement Learning for Minimizing Age-of-Information in UA V- Assisted Networks,","venue":null,"work_id":"0f71c39a-9292-47b8-8f76-4ea8d030d852","year":2019},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.222337Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:635328fb927bdaba95dcee3b637bc84732c91d6740d99c9a7866791a26a7e75e","observation_id":"53638781-a734-478e-b914-3317389e8831","resolution":{"observed_at":"2026-08-06T16:45:57.681586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.400687Z","title":"Improved Denoising Diffusion Prob- abilistic Models,","venue":null,"work_id":"ae2a6b76-77fb-49ad-8a42-953f6d1ebb9c","year":2021},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.312381Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:422d934ef98a684c5571c4b71f8c48930ebaf50cb342fac8308effe6460de45d","observation_id":"c7cf7cff-f047-4bde-87f5-5968d77106f2","resolution":{"observed_at":"2026-08-06T16:45:57.492674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.248302Z","title":"Denoising Diffusion Probabilistic Models, author=Ho, Jonathan and Jain, Ajay and Abbeel, Pieter,","venue":null,"work_id":"7804c26c-cc29-4f96-b19c-62eb8af271ab","year":2020},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.401767Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:dace1e582c27a63e25ba5553cb6171639a3fa94c21c36714598076c6843fdc6f","observation_id":"e873a481-1b0f-4078-b6ec-bb4f23baabef","resolution":{"observed_at":"2026-08-06T16:45:57.308745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:57.096265Z","title":"Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services,","venue":null,"work_id":"c0b252a9-e6f3-4dc4-b183-ee40421862c0","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.494192Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:11fda2107cf56fd2f5630c45341005632a535b3f9aeed8583ea17bae642694b5","observation_id":"56fe6382-c844-40ff-94be-87b6ad786575","resolution":{"observed_at":"2026-08-06T16:45:57.160689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:56.933402Z","title":"Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL,","venue":null,"work_id":"590c1629-2c2e-4e90-a076-5be30a10ba3c","year":2024},"citing_paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:56.582881Z"},"links":{"citing_paper":"/paper/2507.12910"},"observation_digest":"sha256:fe6adc6f6dfef19add79d7efe41fedab7efb858c77e7dadce01f4644d46ed817","observation_id":"c4a14f72-01dd-4d90-9419-17ef4bc45e43","resolution":{"observed_at":"2026-08-06T16:45:56.978205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.12910","last_updated":"2025-07-17T08:57:21Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-06T16:32:53.819781Z","submitted_at":"2025-07-17T08:57:21Z","title":"Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":37},"total_outbound_references":44},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2507.12910."}