{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SO4TAI6XDWNQTOECGJBZUVNMQR","short_pith_number":"pith:SO4TAI6X","schema_version":"1.0","canonical_sha256":"93b93023d71d9b09b88232439a55ac845203f9a01730839587d5b499c666c1ea","source":{"kind":"arxiv","id":"2603.06607","version":2},"attestation_state":"computed","paper":{"title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.MA","authors_text":"Kan Zheng, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Siyuan Wang","submitted_at":"2026-02-18T14:46:56Z","abstract_excerpt":"Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robustness and generalization, are often intertwined, making it difficult to assess their individual impact on performance in vehicular environments. Moreover, exi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2603.06607","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2026-02-18T14:46:56Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"655fe3984ff6dc8732f920295e6654b02da207d723e1807f1d6bb7bf2ec9f01a","abstract_canon_sha256":"0da86dd3c5e111313c3d7ff9277962fc1e9e7ee2888f8f6ab6e4b71394590305"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T03:18:54.546392Z","signature_b64":"u066y0fse5j8i+B3MpTfPutHm/+VxewIu6cOC/3ExnWyFyJ7cGcx3WvMjjD6KnNt9i7+KvxA2e8iCJDtDWkTCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93b93023d71d9b09b88232439a55ac845203f9a01730839587d5b499c666c1ea","last_reissued_at":"2026-07-07T03:18:54.545819Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T03:18:54.545819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.MA","authors_text":"Kan Zheng, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Siyuan Wang","submitted_at":"2026-02-18T14:46:56Z","abstract_excerpt":"Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robustness and generalization, are often intertwined, making it difficult to assess their individual impact on performance in vehicular environments. Moreover, exi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.06607","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.06607/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2603.06607","created_at":"2026-07-07T03:18:54.545887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.06607v2","created_at":"2026-07-07T03:18:54.545887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.06607","created_at":"2026-07-07T03:18:54.545887+00:00"},{"alias_kind":"pith_short_12","alias_value":"SO4TAI6XDWNQ","created_at":"2026-07-07T03:18:54.545887+00:00"},{"alias_kind":"pith_short_16","alias_value":"SO4TAI6XDWNQTOEC","created_at":"2026-07-07T03:18:54.545887+00:00"},{"alias_kind":"pith_short_8","alias_value":"SO4TAI6X","created_at":"2026-07-07T03:18:54.545887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR","json":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR.json","graph_json":"https://pith.science/api/pith-number/SO4TAI6XDWNQTOECGJBZUVNMQR/graph.json","events_json":"https://pith.science/api/pith-number/SO4TAI6XDWNQTOECGJBZUVNMQR/events.json","paper":"https://pith.science/paper/SO4TAI6X"},"agent_actions":{"view_html":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR","download_json":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR.json","view_paper":"https://pith.science/paper/SO4TAI6X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.06607&json=true","fetch_graph":"https://pith.science/api/pith-number/SO4TAI6XDWNQTOECGJBZUVNMQR/graph.json","fetch_events":"https://pith.science/api/pith-number/SO4TAI6XDWNQTOECGJBZUVNMQR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR/action/storage_attestation","attest_author":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR/action/author_attestation","sign_citation":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR/action/citation_signature","submit_replication":"https://pith.science/pith/SO4TAI6XDWNQTOECGJBZUVNMQR/action/replication_record"}},"created_at":"2026-07-07T03:18:54.545887+00:00","updated_at":"2026-07-07T03:18:54.545887+00:00"}