{"as_of":"2026-08-09T08:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c240993af122db58175852c79f2ffa3968e8db36a87a6ae245618f70e48ae900","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T15:03:59.487746Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"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/2608.05340/citation-record","integrity":"/paper/2608.05340/integrity","json":"/paper/2608.05340/citation-record.json","paper":"/paper/2608.05340"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T15:03:59.669349Z","title":"Edge learning via federated split decision transformers for metaverse resource allocation,","venue":null,"work_id":"59daf0c5-7ed2-48d0-bae8-bdb5a400c78b","year":2026},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.431609Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:96972ec0787947417ed7d17112f808d6b044529800ab6928cbd6bf931426f944","observation_id":"d4c644aa-6445-48ac-bdb5-624df265f6a6","resolution":{"observed_at":"2026-08-08T15:03:59.673153Z","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":"2026-08-08T15:03:59.660596Z","title":"Self-play ensemble q-learning enabled resource allocation for network slicing,","venue":null,"work_id":"a56b86e0-3d2f-4ce2-93d0-090d0eaebdee","year":2024},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.435791Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:5954630782ee933f479f2518db771f05903beacbc2a1108a93025773ae73caa9","observation_id":"3a50d94d-ae75-48fd-8bf8-998ec914dfe4","resolution":{"observed_at":"2026-08-08T15:03:59.663381Z","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":"2026-08-08T15:03:59.438753Z","title":"Performance analysis of the integra- tion of dynamic cloud computing environments and tsn networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.438753Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:83689e784b553b96ef971903950fcd8c49c8b3d67cfdeec94040b94d73470316","observation_id":"4fce180c-0680-446e-8b79-d84522b33428","resolution":{"observed_at":"2026-08-08T15:03:59.438753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T15:03:59.441497Z","title":"IEEE Standard for local and metropolitan area networks—bridges and bridged networks—amendment 25: Enhancements for scheduled traffic,IEEE Standard 802.1qbv-2015, 2016, pp. 1–57,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.441497Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:9e7b0013b27f7c0daaff0e04e70e97ebe6761b250cc333df52d98301b16f00a4","observation_id":"3dde7e31-9e2a-444c-9432-d3e8b698eea7","resolution":{"observed_at":"2026-08-08T15:03:59.441497Z","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":"2026-08-08T15:03:59.641315Z","title":"Enhancing mobile immersive streaming experience via deadline-aware scheduling and learning-enhanced congestion control,","venue":null,"work_id":"dbf4e01b-e73a-4182-a02b-5985341830aa","year":2025},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.444662Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:a844949e9c0d4e4074f70674e3dd003d06b71b3b1c0bfa8cddbc39299ef9025b","observation_id":"549a26ff-c0cd-4ad2-b1f9-99596bc29ea6","resolution":{"observed_at":"2026-08-08T15:03:59.644202Z","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":"2026-08-08T15:03:59.447373Z","title":"A survey of schedul- ing algorithms for the time-aware shaper in time-sensitive networking (tsn),","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.447373Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:4e091fe32d87dcb568a07b8a5527979c818daa09c5e43d34e3a85f12658061b6","observation_id":"021a67ab-bf52-490b-b622-ba7b0d83ac70","resolution":{"observed_at":"2026-08-08T15:03:59.447373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T15:03:59.450260Z","title":"Configuring the ieee 802.1 q time-aware shaper with deep reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.450260Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:d8657f80dae6a32bea7fd527cabc56679af9000e3b9d1edd8860ad14728b4dc4","observation_id":"7ca1db81-7219-4984-bf9d-c028978da431","resolution":{"observed_at":"2026-08-08T15:03:59.450260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T15:03:59.452771Z","title":"Mitigation of scheduling violations in time- sensitive networking using deep deterministic policy gradient,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.452771Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:9992d328b5de8df2983d327295294d449bdef315cf15d9dddf2981fa7165f037","observation_id":"a9ad734f-c7bd-4ff0-9d34-e0edb6578b0d","resolution":{"observed_at":"2026-08-08T15:03:59.452771Z","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":"2026-08-08T15:03:59.615630Z","title":"Cooperative resource allocation and traffic scheduling for iiot controllers in edge clouds: A hierarchical reinforcement learning approach,","venue":null,"work_id":"17222e4d-bc8c-4482-866c-c23aefcdfc34","year":2025},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.455318Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:99b39a8d8ef4f90117ec6dc8a7270ba29d9e38854f55d8d864c448b248c4260e","observation_id":"407ae727-bd0e-446d-9845-544e0ad5bd91","resolution":{"observed_at":"2026-08-08T15:03:59.618917Z","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":"2026-08-08T15:03:59.606300Z","title":"Towards distributed flow scheduling in ieee 802.1 qbv time-sensitive networks,","venue":null,"work_id":"a14d0826-30aa-4c0a-886c-e6a668a91f11","year":2024},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.457863Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:39cefd7762739e89b8293165530eca579909bc54ffcbb944fbb64c1679862b83","observation_id":"1d106265-0c86-45dd-9a26-85919bce9673","resolution":{"observed_at":"2026-08-08T15:03:59.609755Z","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":"2026-08-08T15:03:59.596950Z","title":"Multi-agent reinforcement learning-based routing and scheduling models in time-sensitive networking for internet of vehicles communi- cations between transportation field cabinets,","venue":null,"work_id":"03863deb-4158-4268-a968-8fb114d883d1","year":2025},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.460324Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:8aa0ccec48827fdf00a9938df725c33f19aa057272a8d49261c0e5a3fc453481","observation_id":"b567b72c-23f5-4476-89b5-5e1c267f6f1d","resolution":{"observed_at":"2026-08-08T15:03:59.600393Z","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":"2026-08-08T15:03:59.587168Z","title":"Sharp: A study on safe heterogeneous agent reinforcement learning paradigm for 5g-tsn traffic scheduling,","venue":null,"work_id":"8bbf54c6-3327-4428-91ef-6f9b2b96770d","year":2025},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.463401Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:6e1a3557b71fa34b75be37df40438d9459cb538508dfd24e481cb807dde25719","observation_id":"6a8aaa2a-418e-4b1a-801c-7babab3a70cb","resolution":{"observed_at":"2026-08-08T15:03:59.590494Z","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":"2026-08-08T15:03:59.577798Z","title":"Multi-agent reinforcement learning is a sequence modeling problem,","venue":null,"work_id":"218c0276-c97d-46ab-ad8d-03aa185619d5","year":2022},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.466535Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:77d70fce57944aa567c9053b9a1c23fabe3eb147dd5f098fcfc23b8a6a81b633","observation_id":"5f2d1e32-8e19-4652-b8b6-6c0384fbfee2","resolution":{"observed_at":"2026-08-08T15:03:59.581099Z","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":"2026-08-08T15:03:59.566327Z","title":"Semantic communi- cations in networked systems: A data significance perspective,","venue":null,"work_id":"a57e89f8-d641-4cfb-82d0-2aec0d35ba7d","year":2022},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.469478Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:6fcc146e4bfad14948719e01b9f4319154551da707ac7ab36b5b02a79caf9c9c","observation_id":"b19e441b-f2b8-4850-8b49-e43fd1d6340e","resolution":{"observed_at":"2026-08-08T15:03:59.570697Z","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":"2026-08-08T15:03:59.472439Z","title":"An extended reality offloading ip traffic dataset and models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.472439Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:1268a647491d7df55d52feefddc7e32d4a02b15489b14a669fd875ef91a0c838","observation_id":"ea55cb7d-5f7c-400a-841b-e60542d5a3d2","resolution":{"observed_at":"2026-08-08T15:03:59.472439Z","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":"2026-08-08T15:03:59.548688Z","title":"Methodology and infrastructure for tsn-based reproducible network experiments,","venue":null,"work_id":"74a69f2b-6a33-4cb7-8d8c-6009cd159025","year":2022},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.475579Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:5f773e32d25a1dd768b2d4e3db75e4524130bb747864e8402cb67b2d96cffbdc","observation_id":"dc9e060f-c566-4d25-ade2-65abea0f09ab","resolution":{"observed_at":"2026-08-08T15:03:59.552264Z","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":"2026-08-08T15:03:59.538167Z","title":"Pdu-set scheduling algorithm for xr traffic in multi-service 5g-advanced networks,","venue":null,"work_id":"3b059949-c665-4dcc-b1f0-2cea2203e7f9","year":2024},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.478590Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:38603c2756f7eb94c4441522c74148b94ba983a09b1595aff2e0e320198ec565","observation_id":"4158fd78-7d1c-43d9-9f3d-9857b425fecf","resolution":{"observed_at":"2026-08-08T15:03:59.541636Z","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":"2026-08-08T15:03:59.526244Z","title":"Multi-agent transformer approach for collaborative task offloading and resource optimization in noma-based vehicular edge computing,","venue":null,"work_id":"e5f323bc-abb6-44e0-8376-6419fcf67d7e","year":2026},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.481689Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:320de12794b181e221a06cdc22a9e5ebff521071bc8ddecdd247831125dffef7","observation_id":"f7bcd007-7604-4193-9441-96e9f37bdc8a","resolution":{"observed_at":"2026-08-08T15:03:59.531182Z","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":"2026-08-08T15:03:59.484833Z","title":"Asynchronous methods for deep rein- forcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.484833Z"},"links":{"citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:796c0f00b20e9c8c82d844b3061c152e166f97111904fa333fe2d5627c95bc0a","observation_id":"42c4b036-5321-495e-9912-a3a022640b46","resolution":{"observed_at":"2026-08-08T15:03:59.484833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11251","last_updated":"2022-04-04T06:39:20Z","snapshot_observed_at":"2026-08-06T03:41:51.704789Z","submitted_at":"2021-09-23T09:44:35Z","title":"Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.11251","snapshot_observed_at":"2026-08-08T15:03:59.487746Z","title":"Trust region policy optimisation in multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T15:03:59.487746Z"},"links":{"cited_paper":"/paper/2109.11251","citing_paper":"/paper/2608.05340"},"observation_digest":"sha256:a61902595d4ab5834cae2867ba515c0133ba45d3418f0084383318fb23467b47","observation_id":"748a4a37-ff95-438e-836e-6ee1bd1fd78b","resolution":{"observed_at":"2026-08-08T15:03:59.487746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.05340","last_updated":"2026-08-05T18:57:24Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-09T08:10:45.951468Z","submitted_at":"2026-08-05T18:57:24Z","title":"Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":20},"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 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2608.05340."}