{"as_of":"2026-08-06T17:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b6d12090f0504a89990e0cfa3a1b765004068b544078a338d990f020b80e69d4","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T22:34:20.119341Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2603.06607/citation-record","integrity":"/paper/2603.06607/integrity","json":"/paper/2603.06607/citation-record.json","paper":"/paper/2603.06607"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T22:34:16.093903Z","title":"Multi-agent drl for resource allocation in vehicular networks: A comparative study,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.093903Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:d2b177f205890700c9cd6bd10577cd3df40c86ae6f7d8d050edaa6689c9b7bbf","observation_id":"9f1c05bb-9e6e-47b4-9ede-71541b8b4d0d","resolution":{"observed_at":"2026-08-02T22:34:16.093903Z","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":"2026-08-02T22:34:16.216191Z","title":"Recent advances and future trends in vehicular communication systems: A comprehensive survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.216191Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:b0d517eee732ba82a146a8c7a60cdc986b60b0b6cf024ed263ed3bfa4642e998","observation_id":"374ba54c-e49a-4153-9a02-3b7532b17dcb","resolution":{"observed_at":"2026-08-02T22:34:16.216191Z","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":"2026-08-02T22:34:16.365192Z","title":"Multiuser resource control with deep reinforcement learning in iot edge comput- ing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.365192Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:82b641756b612948a38dd102e3f81b84fa00b6623c111b45029da4d0fa52cbc1","observation_id":"dd203d4d-334f-4b85-8187-2b9b1989a1cb","resolution":{"observed_at":"2026-08-02T22:34:16.365192Z","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":"2026-08-02T22:34:16.527546Z","title":"Deep reinforcement learning based resource allocation for v2v communications,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.527546Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:052f83fd30beb820df45d4f790186a3130f42eb47c460c766822821c1488eec6","observation_id":"4cc0e15b-acfc-4ae4-af43-c86f058257f7","resolution":{"observed_at":"2026-08-02T22:34:16.527546Z","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":"2026-08-02T22:34:16.599296Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.599296Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:b0caf5125983632f72b283876177fc2dfcbebf025d8f942b59e49f7bb5a0f722","observation_id":"106749c0-e2ce-4b2d-93cc-88533a1678de","resolution":{"observed_at":"2026-08-02T22:34:16.599296Z","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":"2026-08-02T22:34:16.661898Z","title":"Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.661898Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:8a4252b7988de62a6b059b81585a2484dfb884fd2837408c1e975968b7c59b6b","observation_id":"511f70b3-1e08-43aa-a109-56bc96060968","resolution":{"observed_at":"2026-08-02T22:34:16.661898Z","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":"2026-08-02T22:34:16.707973Z","title":"Multi-agent RL enables decentralized spectrum access in vehicular networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.707973Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:1d18a7dd5c28c9e33a47cf1010f3e1c3ac58d4b5adad397e63db8475559b7005","observation_id":"fbb8b2fa-a04a-416b-a21e-4a6446d2c666","resolution":{"observed_at":"2026-08-02T22:34:16.707973Z","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":"2026-08-02T22:34:16.766409Z","title":"Multi-agent reinforcement learning-based decentralized spectrum access in vehic- ular networks with emergent communication,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.766409Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:d1de7cb4dc69819bf2e80751e695c5e1a7c95b963e51550076b035d8e739d771","observation_id":"3cb9e088-f01e-43b0-bf8b-e8502403d006","resolution":{"observed_at":"2026-08-02T22:34:16.766409Z","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":"2026-08-02T22:34:16.825818Z","title":"Resource allocation in V2X communications based on multi-agent reinforcement learning with attention mechanism,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.825818Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:2092ac2d7aef61dc3054108e956c20889a71fdf6febb27ee320c3b3cdedf4125","observation_id":"982bb0da-cf4c-480c-9155-70b5c1cc87b4","resolution":{"observed_at":"2026-08-02T22:34:16.825818Z","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":"2026-08-02T22:34:16.897576Z","title":"Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.897576Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:3e3098a531a1e12ce0455a92053c154ba69ade6dfe672601bf2c54df3fb07885","observation_id":"74fef6dc-c209-4b86-a522-151d46cd5c62","resolution":{"observed_at":"2026-08-02T22:34:16.897576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06518","last_updated":"2025-06-16T08:00:30Z","snapshot_observed_at":"2026-07-06T18:43:26.425575Z","submitted_at":"2024-07-09T03:14:11Z","title":"Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06518","snapshot_observed_at":"2026-08-02T22:34:16.986345Z","title":"Graph neural networks and deep reinforcement learning based resource allocation for v2x communications,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:16.986345Z"},"links":{"cited_paper":"/paper/2407.06518","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:bf0401bcf1f10356e65541e1c047bd5039e0e900f3ed17e7ee4573b73fca2f28","observation_id":"ac01e186-28d3-4e1f-ad11-5c21aa76f15e","resolution":{"observed_at":"2026-08-02T22:34:16.986345Z","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":"2026-08-02T22:34:17.175140Z","title":"Meta-reinforcement learning based resource allocation for dynamic v2x communications,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.175140Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:6e30f2807d293f1303777b4b6ab928588042f4d3d36f04837602046584b787dd","observation_id":"731b35d2-d8bf-493f-adc8-fe57b2b25cd8","resolution":{"observed_at":"2026-08-02T22:34:17.175140Z","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":"2026-08-02T22:34:17.269895Z","title":"Multitimescale control and communications with deep reinforcement learning—part ii: Control- aware radio resource allocation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.269895Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:a2ce58dcba06aa149d92e61c282cb1f47ad71f75c020df6317794913d1b85ab0","observation_id":"10b5bb66-4620-4565-8cc3-9b10e507eb35","resolution":{"observed_at":"2026-08-02T22:34:17.269895Z","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":"2026-08-02T22:34:17.389777Z","title":"A review of cooperative multi-agent deep reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.389777Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:8ba1178f469267df735e835240c1c0157309c677308a26ea18a2d23c4e492270","observation_id":"7963d358-bd87-40f6-b64f-5b9962f01b18","resolution":{"observed_at":"2026-08-02T22:34:17.389777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-07-06T09:28:53.031338Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-02T22:34:17.452139Z","title":"Bench- marking multi-agent deep reinforcement learning algorithms in cooper- ative tasks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.452139Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:5c16917cdab35cdb493dfa20ba24f96f26db18e464e5a9bd67fc8628006feb28","observation_id":"df126020-9847-4c4d-94d5-6dc1312ef6d7","resolution":{"observed_at":"2026-08-02T22:34:17.452139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-02T22:34:17.513122Z","title":"Prox- imal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.513122Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:faffbb11b0ff0d463e8063d341a25cf185686828f2a31c74ce2504af84f89fa3","observation_id":"3cced00c-6889-4909-a1d6-e9c04a97e1bb","resolution":{"observed_at":"2026-08-02T22:34:17.513122Z","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":"2026-08-02T22:34:17.611716Z","title":"Deep reinforcement learning based resource allocation with heterogeneous qos for cellular v2x,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.611716Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:8723fc3bbb3d2dfc3838e6f27917402d40067d34806730e0933219f1c2c83da1","observation_id":"3320a770-529f-4dbc-b0a6-571027f34d67","resolution":{"observed_at":"2026-08-02T22:34:17.611716Z","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":"2026-08-02T22:34:17.673929Z","title":"Spectrum-energy-efficient mode selection and resource allocation for heterogeneous v2x networks: A fed- erated multi-agent deep reinforcement learning approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.673929Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:73b101e414250820e1207a14f9700fe44d470b414962f2261be1ce0e16771f40","observation_id":"9a3779d8-374c-4d97-901a-1b71e5e04ff9","resolution":{"observed_at":"2026-08-02T22:34:17.673929Z","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":"2026-08-02T22:34:17.746470Z","title":"A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.746470Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:0bad76c2d27fa5acf2021c0e7b016c8cfa25f5f32ff87573df142d9fc95a9752","observation_id":"b764cb70-958b-469f-bc43-f3fcb156e0ac","resolution":{"observed_at":"2026-08-02T22:34:17.746470Z","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":"2026-08-02T22:34:17.848633Z","title":"Federated reinforcement learning for resource allocation in v2x networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.848633Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:b4938fdeff6d371a7f6066d6049f497a5d6fe0fa18f76efad62000be1a72fd2c","observation_id":"4d9c1c0a-e034-42f3-95f8-ced5e59cf4bb","resolution":{"observed_at":"2026-08-02T22:34:17.848633Z","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":"2026-08-02T22:34:17.985066Z","title":"Semantic- aware resource allocation based on deep reinforcement learning for 5g- v2x hetnets,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.985066Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:b82d4c368bd63cd4b1640eadcec2a2a245ad98c9e087f93a92acc923bf6b7d6f","observation_id":"49bb79f2-027a-42cc-a08c-18958143a13b","resolution":{"observed_at":"2026-08-02T22:34:17.985066Z","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":"2026-08-02T22:34:18.064152Z","title":"Deep reinforcement learning for multi- objective resource allocation in multi-platoon cooperative vehicular networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.064152Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:5b9d90c797e218701c1e631122bea7e7dc71dc947ac1ba4d28c5a6f874b0e802","observation_id":"7360ce8d-3795-4b10-8e6c-16d6fad6459b","resolution":{"observed_at":"2026-08-02T22:34:18.064152Z","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":"2026-08-02T22:34:18.179888Z","title":"Enabling adaptive optimization of energy efficiency and quality of service in nr-v2x communications via multiagent deep reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.179888Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:89b679a762f4438159b3ce7800e927163ecb1ffe8475ede7cd4d370f0fa22fc2","observation_id":"01bacd2b-7915-4c8d-a8e2-11ed06589450","resolution":{"observed_at":"2026-08-02T22:34:18.179888Z","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":"2026-08-02T22:34:18.300005Z","title":"Aoi-aware resource allocation for platoon-based c-v2x networks via multi-agent multi-task reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.300005Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:8711fe533dc843a63055b88d06fcd2282b58d920c6843758c5e186c205f47709","observation_id":"cf45f331-e31c-4e13-80a1-93390e7ff969","resolution":{"observed_at":"2026-08-02T22:34:18.300005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04672","last_updated":"2025-05-26T12:55:04Z","snapshot_observed_at":"2026-08-06T15:49:04.305390Z","submitted_at":"2024-11-07T12:55:35Z","title":"Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04672","snapshot_observed_at":"2026-08-02T22:34:18.388395Z","title":"Semantic-aware resource management for c-v2x platooning via multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.388395Z"},"links":{"cited_paper":"/paper/2411.04672","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:e5d7675a8593007f63eb3fa87fe4753c27657d1e00ab0c5f4221b03912488008","observation_id":"b0d3b2ca-6998-4be9-9d08-749536e122d2","resolution":{"observed_at":"2026-08-02T22:34:18.388395Z","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":"2026-08-02T22:34:18.491079Z","title":"Deep reinforcement learning for autonomous internet of things: Model, ap- plications and challenges,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.491079Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:af1118492c94fc3e1670ef224198b87dfd0b88fa121a74e2f17f5a5ad47ea47e","observation_id":"531f6618-ee62-4690-b966-43f7a7cc73d4","resolution":{"observed_at":"2026-08-02T22:34:18.491079Z","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":"2026-08-02T22:34:18.617606Z","title":"Multi-agent actor-critic for mixed cooperative-competitive environ- ments,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.617606Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:8c58e3ef336c4ff40a4f71a55c964bda99d0a341bf0adf5b977f1cbf1bcaa7ab","observation_id":"74ad8ce7-7220-41e9-ad32-70887933cb06","resolution":{"observed_at":"2026-08-02T22:34:18.617606Z","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":"2026-08-02T22:34:18.746179Z","title":"Continuous control with deep reinforcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.746179Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:24de830089aba38cb505ea18b8f1bcab3a7e0b5e2fad2532f850355e775baf73","observation_id":"0304c7ea-5099-4db4-8b3c-13e24a6d7f3a","resolution":{"observed_at":"2026-08-02T22:34:18.746179Z","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":"2026-08-02T22:34:18.826908Z","title":"Technical Specification Group Radio Access Network; Study LTE- Based V2X Services; (Release 14),","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.826908Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:ad8c513895a902c4c79ff152e23ad7eab3c064cf4f3e0786ba3e329d3d1dc42e","observation_id":"7f87d410-b139-483b-8a1c-ba2f563cd3b3","resolution":{"observed_at":"2026-08-02T22:34:18.826908Z","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":"2026-08-02T22:34:18.883970Z","title":"TR 103 766, no","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:18.883970Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:3e0edbab597ce4c07d440c9310a61931738cfbe62db4a2d5a9421a094e28a890","observation_id":"b9bb1224-a219-4aa6-a431-c24ecfb621e7","resolution":{"observed_at":"2026-08-02T22:34:18.883970Z","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":"2026-08-02T22:34:19.099121Z","title":"Delay- optimal dynamic mode selection and resource allocation in device-to- device communications—part ii: Practical algorithm,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.099121Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:7df5caaf0d8912d3a8826cdc7899a80606740f2b49d0290d94adb76c01d6e427","observation_id":"17e50fc2-07fd-4da3-aedc-44c9e5989f8a","resolution":{"observed_at":"2026-08-02T22:34:19.099121Z","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":"2026-08-02T22:34:19.227885Z","title":"Lenient learning in independent-learner stochastic cooperative games,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.227885Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:6150385eea65674d22175ddcadf872ecf6ad307a15aecbb6b747cf9c516f502c","observation_id":"ff5f13eb-31a7-42d6-8734-d8ed17dfd325","resolution":{"observed_at":"2026-08-02T22:34:19.227885Z","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":"2026-08-02T22:34:19.418454Z","title":"Leveraging procedural generation to benchmark reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.418454Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:d7ae8391cbbce5142f99935c4522973524efe1dcc6a1770d4c13db2e72f5139c","observation_id":"c4cd68c6-5569-40cd-b67b-0fbc82d50387","resolution":{"observed_at":"2026-08-02T22:34:19.418454Z","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":"2026-08-02T22:34:19.542941Z","title":"Human-level control through deep reinforcement learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.542941Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:07da1d5d62ca4baa314e09c8a83f4b3fd506e7c96b1155bc31b4dba89defb1ee","observation_id":"3921ecd1-545a-474f-be8d-e190ede9d683","resolution":{"observed_at":"2026-08-02T22:34:19.542941Z","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":"2026-08-02T22:34:19.650964Z","title":"Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.650964Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:bab8e9e2e09a13d6c7dd502689358c0f51b37debe01725002ea45d689f4ea81f","observation_id":"e406cb7e-4987-4d1d-9dc0-4edb0a6e72c0","resolution":{"observed_at":"2026-08-02T22:34:19.650964Z","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":"2026-08-02T22:34:19.700786Z","title":"Asynchronous methods for deep reinforcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.700786Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:ca96eb071a2693d9efdc3208d533b44ecec792cb8d42496288130f7474034bd4","observation_id":"2903a1ad-14ec-4221-acec-3c7a8e3cd478","resolution":{"observed_at":"2026-08-02T22:34:19.700786Z","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":"2026-08-02T22:34:19.775302Z","title":"Value-decomposition networks for cooperative multi-agent learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.775302Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:0c39a5dc728b7fab02787b36c775c0104e9a5b9d2e95556c873bb74d954b75e1","observation_id":"d2be844e-5413-44bd-8d73-b66ede30eceb","resolution":{"observed_at":"2026-08-02T22:34:19.775302Z","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":"2026-08-02T22:34:19.853730Z","title":"Qmix: Monotonic value function factorization for deep multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.853730Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:375af96399a4e079742fa87db62aabd98393c30917403a830b270be0cbe48ae4","observation_id":"e221153c-c6f8-404e-b5da-56b312ed82ad","resolution":{"observed_at":"2026-08-02T22:34:19.853730Z","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":"2026-08-02T22:34:19.938432Z","title":"The surprising effectiveness of ppo in cooperative, multi-agent games,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:19.938432Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:08ea26b436ac07b7d740c7e907f953452de59cd238faec2613c1ab5aaa18cc9f","observation_id":"c355a984-4547-42ea-b39f-af769ff4e5ec","resolution":{"observed_at":"2026-08-02T22:34:19.938432Z","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":"2026-08-02T22:34:20.119341Z","title":"Performance analysis of device-to-device communications with dynamic interference using stochastic petri nets,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:20.119341Z"},"links":{"citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:71b2646855b46059243d264467c62ac45f9804fe76bda86b51e46e459d87b948","observation_id":"37ad47fb-712a-4195-8c13-6ba7904afede","resolution":{"observed_at":"2026-08-02T22:34:20.119341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.01955","last_updated":"2022-11-04T06:16:11Z","snapshot_observed_at":"2026-07-06T10:46:11.856932Z","submitted_at":"2021-03-02T18:59:56Z","title":"The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.01955","snapshot_observed_at":"2026-08-02T22:34:20.029978Z","title":"Available: https://arxiv.org/abs/2103.01955","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:20.029978Z"},"links":{"cited_paper":"/paper/2103.01955","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:121a29aa1415d2d9c25251edd727b39bf07905d16c583db8a6353325fb67751c","observation_id":"c60c4bf8-78d6-4bb4-ae37-01190cc2feb3","resolution":{"observed_at":"2026-08-02T22:34:20.029978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","latest_version":2,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-06T03:50:18.321643Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":41},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2603.06607."}