{"as_of":"2026-08-09T10:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd38f104f7b5a0827e16ac5fc2495594ae7452fdc03066db851477ae7615c2d4","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:55:44.166713Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"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/2608.05346/citation-record","integrity":"/paper/2608.05346/integrity","json":"/paper/2608.05346/citation-record.json","paper":"/paper/2608.05346"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:55:44.328275Z","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":"34cf173a-3385-413f-9e7b-7f92b98d8239","year":2015},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.113602Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:72382de36c562670439e8ccd255b4e0e6cf547526fd40bf6876b9713e9e7ca91","observation_id":"57053204-a27f-4240-b346-a17bfa5ca8be","resolution":{"observed_at":"2026-08-08T14:55:44.332010Z","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":"2026-08-08T14:55:44.318565Z","title":"Performance analysis of the integra- tion of dynamic cloud computing environments and tsn networks,","venue":null,"work_id":"d5528afa-0c96-451d-b395-afb788c27ec5","year":2025},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.118765Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:10c5b28a6ef224fdda96ae346382edf5fff3718d5f8004d4a90b21ea8241348d","observation_id":"0eb5a907-d373-4f8c-ab1b-5332e823d1a1","resolution":{"observed_at":"2026-08-08T14:55:44.322058Z","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":"2026-08-08T14:55:44.308794Z","title":"A survey of schedul- ing algorithms for the time-aware shaper in time-sensitive networking (tsn),","venue":null,"work_id":"13bfa812-5ada-46bb-8a44-47a65e994603","year":2023},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.122621Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:8d95157bc88ea90d72ab2a33638be0894160b28af512d0a6528030662356b1b8","observation_id":"b60c8e3e-27ef-40d7-b94e-110eaa6da038","resolution":{"observed_at":"2026-08-08T14:55:44.312254Z","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":"2026-08-08T14:55:44.126279Z","title":"Time- sensitive networking (tsn) for industrial automation: Current advances and future directions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.126279Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:b4e614cbd0310eced363244829aa19779ef93bb044ec582db9e2e5300a0b688d","observation_id":"80a45ce0-9596-42f8-83a0-8c8847be8587","resolution":{"observed_at":"2026-08-08T14:55:44.126279Z","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":"2026-08-08T14:55:44.293102Z","title":"Reinforce- ment learning based routing for time-aware shaper scheduling in time- sensitive networks,","venue":null,"work_id":"260196f2-d745-4246-b49a-2190f7890aa4","year":2023},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.130235Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:5a7b56537076e594c6d9ab85ef776c7bef98639e5da18b3bee0b32e9dcc123f2","observation_id":"96a4b31d-0d29-410d-82aa-85aab096d62a","resolution":{"observed_at":"2026-08-08T14:55:44.296903Z","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":"2026-08-08T14:55:44.282383Z","title":"Deepscheduler: En- abling flow-aware scheduling in time-sensitive networking,","venue":null,"work_id":"5c00e4b5-6f23-43b1-9d8a-52aaa2167b53","year":2023},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.134060Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:a29c26d8522e9b00de9ea0590ebca54df283a0dd1b01e9d5c6b2dbc3b56fd1c3","observation_id":"815aac74-ee9e-4a5f-ad8e-cbf6ee7b9fe1","resolution":{"observed_at":"2026-08-08T14:55:44.286109Z","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":"2026-08-08T14:55:44.271562Z","title":"Ai-based dynamic schedule calculation in time sensitive networks using gcn-td3,","venue":null,"work_id":"addbf6c4-25b8-4336-971a-ac72caff23e6","year":2024},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.138191Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:10128b0a4dacd4ac0298934c8d0285f4054b4f77d4603e6f3792314305be6def","observation_id":"96de13d7-4d49-4822-b629-5f381c55b06c","resolution":{"observed_at":"2026-08-08T14:55:44.275316Z","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":"2026-08-08T14:55:44.259630Z","title":"Deterministic scheduling for asymmetric flows in future wireless networks,","venue":null,"work_id":"09da9199-d7d7-48b3-91dd-c403f0807c15","year":2025},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.141614Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:cda98746feaa13a990bc7260f761b20c0fd6e14e475cda27c249a46074a51137","observation_id":"113a21a9-e6b6-4390-93c1-3768354ed98d","resolution":{"observed_at":"2026-08-08T14:55:44.263711Z","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":"2026-08-08T14:55:44.248150Z","title":"Configuring the ieee 802.1 q time-aware shaper with deep reinforcement learning,","venue":null,"work_id":"c9e9bf1c-2c80-4398-a5ac-9e10c5d97ab4","year":2024},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.145172Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:0bbc63a1e30785f8070d556c6586e9dc710f418d36144f5a7db20cb9c608443f","observation_id":"2cba989a-937f-45e1-849f-3462329e8ccd","resolution":{"observed_at":"2026-08-08T14:55:44.251762Z","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":"2026-08-08T14:55:44.238837Z","title":"Mitigation of scheduling violations in time- sensitive networking using deep deterministic policy gradient,","venue":null,"work_id":"26b710ea-b269-4bf4-89ae-98dcd7bed8d2","year":2021},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.148636Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:f0cd1749f2c82f807aabd0624ad309fe431001a114b09df1ba219dbcf5e6254e","observation_id":"90c7b170-a180-4498-8adf-c53e44723bdd","resolution":{"observed_at":"2026-08-08T14:55:44.241755Z","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":"2026-08-08T14:55:44.230124Z","title":"Convergence of reinforcement learning and time-sensitive networking for future industrial ai agent communication: Fundamentals, challenges, and opportunities,","venue":null,"work_id":"b49bc675-ec42-4fb8-8769-e8d47f8e0c3a","year":2026},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.152160Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:70a16cf12d3c6cd4cd36b9fc6b198e7d77a08b2c0f69999c82469e8d82fa9864","observation_id":"85c25f2b-3f7e-4ab7-bcc7-7ed2468caa35","resolution":{"observed_at":"2026-08-08T14:55:44.233106Z","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":"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":"2026-08-08T14:55:44.155987Z","title":"Trust region policy optimisation in multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.155987Z"},"links":{"cited_paper":"/paper/2109.11251","citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:67a4096a1c9e062b179a91f919c3b177c69eda3f820210b06c72e8059cde64ec","observation_id":"2218156e-1c60-4456-9cef-55a246be9aff","resolution":{"observed_at":"2026-08-08T14:55:44.155987Z","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":"2026-08-08T14:55:44.221300Z","title":"An extended reality offloading ip traffic dataset and models,","venue":null,"work_id":"ef4ef85f-6266-46d9-8f56-2ae2a89e8443","year":2023},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.159932Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:c3de738d3233b69828dcf813aac07a4017dc2bd0ec6706db8ebcab6abddd198a","observation_id":"11fbb440-3834-4c89-bfcb-3ab2a5cb1ccb","resolution":{"observed_at":"2026-08-08T14:55:44.224407Z","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":"2026-08-08T14:55:44.209364Z","title":"From pixels to packets: Traffic classification of augmented reality and cloud gaming,","venue":null,"work_id":"c9c6fa36-cc8f-408a-8f66-9eeb06508211","year":2024},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.163341Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:7dab0588bf9cb9e75d86306fe4b32a12dc30915e96cef2200550556e42a549fe","observation_id":"05b957dc-ad1e-48c6-b0cc-44fe82988b5f","resolution":{"observed_at":"2026-08-08T14:55:44.214761Z","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":"2026-08-08T14:55:44.166713Z","title":"Asynchronous methods for deep rein- forcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T14:55:44.166713Z"},"links":{"citing_paper":"/paper/2608.05346"},"observation_digest":"sha256:50ab356998c344e4a778b95bd41eb03b379bbad1d5aeaf69a5d6c9b84e381b49","observation_id":"aa4fd8cf-9285-47ba-8cca-5f9fd690fa8d","resolution":{"observed_at":"2026-08-08T14:55:44.166713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.05346","last_updated":"2026-08-05T19:06:42Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-09T09:10:10.607639Z","submitted_at":"2026-08-05T19:06:42Z","title":"Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":15},"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 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.05346."}