{"as_of":"2026-08-09T10:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:509f948880537050a650d0bdc5988e8c941e9b0cc259e0508857307831999d60","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T04:20:56.532751Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.03026/citation-record","integrity":"/paper/2608.03026/integrity","json":"/paper/2608.03026/citation-record.json","paper":"/paper/2608.03026"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:20:57.310233Z","title":"AI-native O-RA N archi- tectures for 6G: Toward real-time adaptation, conflict reso lution, and efficient resource management,","venue":null,"work_id":"f8b84eac-4b79-42d5-804f-9ffbf0514b52","year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.363903Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:bc9fbadc7b36a2d4c873ab65c9fd98b39dd6f7533b7ca388f0e61d2e4dc1cb40","observation_id":"6a1434ef-b1cc-4eff-8cc3-a1f156571fa8","resolution":{"observed_at":"2026-08-08T04:20:57.314800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.297005Z","title":"A Systematic Perspective on Co mmuni- cation Innovations Toward 6G,","venue":null,"work_id":"0069abc2-5c93-42d4-8e78-56f1b29c1ece","year":2023},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.369125Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:2083c539b1196fa648c7ee6229483ad2c6d78c283c337739691e16d9c156c871","observation_id":"31576617-b875-444a-ba66-08c3e2692311","resolution":{"observed_at":"2026-08-08T04:20:57.301534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.283431Z","title":"Joint sensing, communication, and computation for vertical fede rated edge learning in edge perception networks,","venue":null,"work_id":"d3262188-9401-4719-896a-dbb53e6b0e05","year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.373621Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:d73b7f99d890cfb1c0f34992e601b8985456e7c34fe382ba43e0a2b7d1310897","observation_id":"7e4cfaef-3698-4917-ac61-8e7b5bf6801b","resolution":{"observed_at":"2026-08-08T04:20:57.288024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.269900Z","title":"Vision-language mo dels for vision tasks: A survey,","venue":null,"work_id":"296720bd-a33e-4fe7-99c4-2153821d5eb5","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.378358Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:4b2a8d490f720760d6ba99f5d2e1c1c99870cbb11ccc4c647b7709a750fa6484","observation_id":"d52af9e1-d318-488f-af46-9b2d88b01cfa","resolution":{"observed_at":"2026-08-08T04:20:57.274330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.256572Z","title":"Lambo: Large AI model empowered edge intelligence,","venue":null,"work_id":"f1a6c204-91b6-4caf-abec-89cbf09e60eb","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.382698Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:4e949f96eeabd8751aac85ec2a5f08a357562b800301792dd5927f6973d12829","observation_id":"7dd25989-bf2d-445c-93e9-4ba262cd8fa6","resolution":{"observed_at":"2026-08-08T04:20:57.260979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.18457","last_updated":"2026-06-22T09:05:06Z","snapshot_observed_at":"2026-08-01T05:49:12.548999Z","submitted_at":"2026-05-18T14:21:07Z","title":"Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks","version":2},"cited_work":{"arxiv_id":"2605.18457","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.18457","snapshot_observed_at":"2026-08-08T04:20:56.736479Z","title":"Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks","venue":"eess.SP","work_id":"a9688e2e-12c7-45e6-9b70-31da5ee33fb3","year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.387137Z"},"links":{"cited_paper":"/paper/2605.18457","citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:3cdaca2361803baad833477bbf2102736ac3b856ab0946aba2490d2a82f91178","observation_id":"f1e771b8-11fc-4dfa-a525-16eb4c66bf16","resolution":{"observed_at":"2026-08-08T04:20:56.743059Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.242869Z","title":"Key Challenges and Research Directions for Space-Air-Ground Integrated Emergency Com munication Networks,","venue":null,"work_id":"5f816468-cc97-495e-83b4-dd172570cbe4","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.392446Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:5d2084cf369ab93ac56252b46917b91a1fd337d7cf3646c6c5ff481453a19371","observation_id":"eed8dff9-76db-488a-9640-b2cda83db008","resolution":{"observed_at":"2026-08-08T04:20:57.247562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.125869Z","title":"Mobil e edge intelligence for large language models: A contemporary sur vey,","venue":null,"work_id":"95901c22-8428-4624-9b23-35ce8e631ea8","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.397055Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:8edeb69028af4c991ce09c3475f5a54b9c84a11b4e49c0f2859c4d97b22313ce","observation_id":"8c1fd16a-35aa-46b0-a172-1f2c235310dc","resolution":{"observed_at":"2026-08-08T04:20:57.233290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.112144Z","title":"Closing the generalization gap in parameter-efficient federated ed ge learning,","venue":null,"work_id":"6a5455e2-bdcd-4266-a321-e88d345722d9","year":null},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.401163Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:4720b2e38b48fb940d4a0d42e6636a4a0b97333e258848a0c6d26d26741b4981","observation_id":"c4876f69-b46a-465c-b4a8-a08f4c954c7b","resolution":{"observed_at":"2026-08-08T04:20:57.116929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-08T04:20:56.410193Z","title":"Gemini: a family of highly capable multimodal models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.410193Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:8605090bc57f3c00b2f23db5b3cbcba884e604a0044bb9fadc6ed26e08f4e4d4","observation_id":"9958562c-9ceb-4bb2-aae8-1e37696e4a60","resolution":{"observed_at":"2026-08-08T04:20:56.410193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-08T04:20:56.415068Z","title":"Llama 2: Open foundation and fine-tuned chat models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.415068Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:d477bc233458a6a5e53fe5ec5d2c381e23356edef517b06e16b01f6a066c6574","observation_id":"58da7911-9ce8-4178-92c6-8288b1130296","resolution":{"observed_at":"2026-08-08T04:20:56.415068Z","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":"2026-08-08T04:20:57.084904Z","title":"The l arger the merrier? Efficient Large AI model inference in wireless edge networks,","venue":null,"work_id":"e4ae715c-fb81-4749-ad97-4c124b07a6d2","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.420347Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:0a82066854d28e1309a056faf60ee1b884fdb67ff9e4613a9488c666a51ceeec","observation_id":"c4b20dc8-0a92-45e5-88c5-9121e1121338","resolution":{"observed_at":"2026-08-08T04:20:57.089296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.071340Z","title":"Optimal AI model splitting and resource allocation for device-edge co-inference in multi-user wireless sensing s ystems,","venue":null,"work_id":"7cbe9a72-192a-4caa-9357-54321b0e4c0d","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.424480Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:6860be922bb4fc1471dac5746b544f8499933b9cc5269f9e27caa773a70e2ef0","observation_id":"b58f4170-3736-4c57-9a56-257812270dc4","resolution":{"observed_at":"2026-08-08T04:20:57.075868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.057828Z","title":"Large language m odels (llms) inference offloading and resource allocation in clou d-edge com- puting: An active inference approach,","venue":null,"work_id":"6135d345-6d29-471f-991e-8777a7fc8a5a","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.428771Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:861c61cad0535c833c5cb526d90a1515fbbce26bc8cd3b3e65c63dee835db4d8","observation_id":"b91b8796-d9b8-45a3-8520-9c596bc4b8f6","resolution":{"observed_at":"2026-08-08T04:20:57.062471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.044309Z","title":"Mcg-sched: Mu lti- cluster gpu scheduling for resource fragmentation reducti on and load balancing,","venue":null,"work_id":"c519fdf8-8a94-4e38-90fb-231e52f94677","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.432657Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:bf2394c2fdc8b082ebf7e699f667dd0debc38cbd8bb2b5605a36726f91240602","observation_id":"e76976dc-dc2b-4ada-8b53-5ad82bb2dcc6","resolution":{"observed_at":"2026-08-08T04:20:57.048761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.030841Z","title":"Semantic communi cations for image recovery and classification via deep joint source a nd channel coding,","venue":null,"work_id":"9ef3d356-49ed-4a75-9234-bbf1c719c860","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.436818Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:0c612465606118320050d7c536f1063e5b5b7d14bc53fde23d9c99205f7f1860","observation_id":"21b2fee1-de92-4d5b-b8e4-5c3fa1a8ba70","resolution":{"observed_at":"2026-08-08T04:20:57.035350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.017335Z","title":"Semantic communi cation: A survey on research landscape, challenges, and future direc tions,","venue":null,"work_id":"748499a9-d8f8-4b54-8d73-3382d9be7ade","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.440814Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:e49a7f79f7e659fdb3e3e409e58f67ce4f1d56990e5e0c7980148a62522796a4","observation_id":"57787b19-7039-452b-9711-cacdb5044b5d","resolution":{"observed_at":"2026-08-08T04:20:57.021987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.003614Z","title":"Adaptable semanti c compression and resource allocation for task-oriented communications ,","venue":null,"work_id":"7748ad9e-958b-4001-88ad-a2acb227d03f","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.444896Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:4d98eeeb5849c2c7674a0f3ad53cda4b0350a6dcbf99df92f0fcf968bd61b9bc","observation_id":"d5df53c0-1056-43c7-a988-3122a7537857","resolution":{"observed_at":"2026-08-08T04:20:57.008248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.989936Z","title":"Semantic communication meets edge intelligence : Semantic- relay-aided text transmissions,","venue":null,"work_id":"079472bc-791f-4c02-8159-385763f40fb4","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.448940Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:8788e8d094074a6d305ac2b129eaf5a965ea80bc63b662a292527b0aba2e9004","observation_id":"b3a29014-d9ce-4648-baab-88c477a5a856","resolution":{"observed_at":"2026-08-08T04:20:56.994430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.975607Z","title":"Optimal model placeme nt and online model splitting for device-edge co-inference,","venue":null,"work_id":"e3467c98-2e84-4853-8118-f8755683b29e","year":2022},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.453037Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:768995b5fdb3f4c8b83a40b7b6adadc9713d18d49f26210b92add278ca2f7c46","observation_id":"aefa74a5-1a11-4b6e-b07a-eed43ce6b12a","resolution":{"observed_at":"2026-08-08T04:20:56.980490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.961228Z","title":"Task- oriented sensing, computation, and communication integra tion for multi- device edge AI,","venue":null,"work_id":"f42deb5c-f787-4e87-928b-9d4c116b197a","year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.457113Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:6ffbde40e5bf5af810d78d809622db21532e53d346fb4c49f21bc5d1a3d9cdbc","observation_id":"f66a6cc2-0828-4044-b1ae-42ca88ca61cb","resolution":{"observed_at":"2026-08-08T04:20:56.966359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.947888Z","title":"Beyond the cloud: Edge inference for generativ e large language models in wireless networks,","venue":null,"work_id":"7931dec0-c4ca-4d3b-80b1-ee9c9986c18d","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.461167Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:96a356e88d85d8b5acc61a783bb7ca45162a92383112e29daaffa65d6d4605fe","observation_id":"5fd0a214-9a31-4467-aff9-4f2a050b86c3","resolution":{"observed_at":"2026-08-08T04:20:56.952208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02616","last_updated":"2024-09-11T11:59:25Z","snapshot_observed_at":"2026-08-06T03:14:39.296335Z","submitted_at":"2024-06-03T09:41:42Z","title":"Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02616","snapshot_observed_at":"2026-08-08T04:20:56.465122Z","title":"Adaptive lay er splitting for wireless LLM inference in edge computing: A mo del- based reinforcement learning approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.465122Z"},"links":{"cited_paper":"/paper/2406.02616","citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:d7aba0e5c142fae6eee1037d1da3435eddf95da987730d659489359c183866bc","observation_id":"4657b477-7dbf-4877-8d33-6d4b482daae4","resolution":{"observed_at":"2026-08-08T04:20:56.465122Z","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":"2026-08-08T04:20:56.934542Z","title":"EdgeShar d: Efficient LLM inference via collaborative edge computing,","venue":null,"work_id":"f25dbb89-c6ed-4e9c-b701-19f9f42912e2","year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.469485Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:80813856b2e9ef2ec525d00f00e2e9384f4491f7de18de4331419738299bcacf","observation_id":"1f1d496c-59d2-4303-833f-8b410abe12cd","resolution":{"observed_at":"2026-08-08T04:20:56.938891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.921110Z","title":"Joint inference offloading and model caching for small and large language mod el collaboration,","venue":null,"work_id":"193957df-db3d-4a36-9a00-896e17e4b48d","year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.473578Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:f95fe886c75de88565f60cb90c71b2e5df8c2d133f52f74e89b99ae16f6a3eac","observation_id":"688373cc-3d95-4872-be64-93e279e17ee4","resolution":{"observed_at":"2026-08-08T04:20:56.925725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.477680Z","title":"Quantization-aware collaborative inference for large em bodied ai mod- els,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.477680Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:1b835e1e43e3014d163c53fae3167f6144fb87e5cfef54c139b1f1ddb3e5dc9e","observation_id":"2cbe9f2f-ec8e-4212-a108-f746e33d0876","resolution":{"observed_at":"2026-08-08T04:20:56.477680Z","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":"2026-08-08T04:20:56.907443Z","title":"Jppo++: Join t power and denoising-inspired prompt optimization for mobile llm ser vices,","venue":null,"work_id":"7f4db488-ebb8-401b-9de5-2af78361e8f3","year":2026},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.482052Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:1135e2b16021f565ee9c3f4116dbad4b7e864ec75f7402ff77cd3b5370327c14","observation_id":"5f0e0287-8fea-46ef-9da6-e82cf71d03fc","resolution":{"observed_at":"2026-08-08T04:20:56.912133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.894032Z","title":"Berger, Rate-distortion theory","venue":null,"work_id":"ac80bb74-a0a9-43ab-8437-b1b4bd1b3332","year":2003},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.486264Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:23848bf7f145ab61d3a48259ecdd33779540c4a58e1081a3637ac7b1034c15da","observation_id":"1e285754-efee-481d-bfdd-18294ca8c420","resolution":{"observed_at":"2026-08-08T04:20:56.898401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.880689Z","title":null,"venue":null,"work_id":"12c10505-2b75-47b2-a013-ba4eec539750","year":1952},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.490510Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:1b119087b9ec799b3b9aa0e99f9d852c173dfe1dd92e1e1dc27bd22be47e4903","observation_id":"ecc3c500-fcdb-4b31-b27c-d040cb28a315","resolution":{"observed_at":"2026-08-08T04:20:56.884906Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.866903Z","title":"Data shapley: Equitable valuat ion of data for machine learning,","venue":null,"work_id":"946563d1-18dd-4c10-b3e3-721474ee21eb","year":2019},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.494583Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:d70ecff99977c8288aff26054908d39e4d3082038ef6f371bf4c00005a9528d3","observation_id":"8cbfa894-b161-43ae-80ee-d64de2029237","resolution":{"observed_at":"2026-08-08T04:20:56.871297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.853404Z","title":"Optimization","venue":null,"work_id":"e7c9409b-2367-4f39-b5ad-6d64f7225c74","year":2021},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.498716Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:96b32a70c09e9493c8c6b9350a23e6cd0f826da7b286f1d93008b8742441e40c","observation_id":"369811a6-3ed9-46b2-b1d9-9950eae97fbc","resolution":{"observed_at":"2026-08-08T04:20:56.857749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.839636Z","title":"Boyd and L","venue":null,"work_id":"8c14c28a-b8fc-4f56-8333-7858ed3d963c","year":2004},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.502976Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:908231b7c76e63074e249cdb33902b98ff9a997de1268aeb0ffafb8f7767b73a","observation_id":"679fa095-9678-4e7a-b982-dc30637f1994","resolution":{"observed_at":"2026-08-08T04:20:56.843959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.825545Z","title":"CVX: Matlab software for discipli ned convex programming,","venue":null,"work_id":"9543eb96-661d-45ca-87dc-d0ae146baed6","year":2016},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.507061Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:644cc0d83a7e60add98e7227a64524b2308e973f9f0915b2a23fd08e1893ff08","observation_id":"5399ab4a-de4f-44b7-8cfc-24de596f177a","resolution":{"observed_at":"2026-08-08T04:20:56.830158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.810081Z","title":"Task-oriented communica tion for mul- tidevice cooperative edge inference,","venue":null,"work_id":"dab43c1f-c493-4295-8b93-0e5c44632787","year":2023},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.511354Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:436c4625acbea15fedec1aceb367971a41d06253bc758ecee48a94b242db3a17","observation_id":"83ebe54a-1e90-4e79-a987-9b5f49008bf7","resolution":{"observed_at":"2026-08-08T04:20:56.814935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.795886Z","title":"MSR-VTT: A large video d escription dataset for bridging video and language,","venue":null,"work_id":"0cf3b5d4-7e55-4f0c-b3b8-e3e2fa03bd27","year":2016},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.515612Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:b14f8418386eae590a2c0df6c6a50f2ce0f5e6a10e081e53246989d9ae03da73","observation_id":"c07eadee-88cb-4d37-8f26-29c69a519e64","resolution":{"observed_at":"2026-08-08T04:20:56.800643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.782119Z","title":"TinyViT: Fast pretraining distillation for small vision t ransformers,","venue":null,"work_id":"13748992-11b2-4c09-9e94-5fb81c23e837","year":2022},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.519888Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:05658b3f227a56ecf40a4e0ed90b713b024e3368165254b513c6a25e8d3053b8","observation_id":"5e9f56fb-127c-4e3f-baa0-7d319ca2970a","resolution":{"observed_at":"2026-08-08T04:20:56.786637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-08T04:20:56.523974Z","title":"Qwen3 technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.523974Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:40712798dfa14696286bf3df18ecb7fadfea9ad2cf5a37f979046cb0cef0e752","observation_id":"6b444cb4-369f-45da-8aa8-24851d0e3046","resolution":{"observed_at":"2026-08-08T04:20:56.523974Z","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":"2026-08-08T04:20:56.767728Z","title":"BLEU: A m ethod for automatic evaluation of machine translation,","venue":null,"work_id":"dcaf1b3f-9555-4951-b798-b4794ceebc10","year":2002},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.528640Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:d93824a5ea38d7365d499d179ab8c43381f787e9d92e38274b09253bf4abba1f","observation_id":"16f2a7b6-7fe8-4d00-b13b-665e4d1de278","resolution":{"observed_at":"2026-08-08T04:20:56.772220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:56.753353Z","title":"CIDEr: Conse nsus-based image description evaluation,","venue":null,"work_id":"db99d527-98bb-4e44-9f2e-14d51a2827a0","year":2015},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.532751Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:6ab75dface09f8224abfad438f13e8ac6c899762d247ef6744c0bcc51daa078c","observation_id":"a20bfd58-8ecf-40c6-be59-9af2060817a8","resolution":{"observed_at":"2026-08-08T04:20:56.757844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T04:20:57.098915Z","title":"Available: https://arxiv.org/abs/2511","venue":null,"work_id":"22be2f8c-f929-464c-baed-61a35ff1e7ec","year":null},"citing_paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-08T04:20:56.405871Z"},"links":{"citing_paper":"/paper/2608.03026"},"observation_digest":"sha256:4097d6f467602a2db19ab1eefe404ced4419be79140219c3b964147e2b737d09","observation_id":"89cbfa6f-61dc-4232-bfb7-5bd150a903e5","resolution":{"observed_at":"2026-08-08T04:20:57.103310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.03026","last_updated":"2026-08-04T02:16:10Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-09T03:36:14.570184Z","submitted_at":"2026-08-04T02:16:10Z","title":"Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":33},"total_outbound_references":40},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.03026."}