{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:N7JOGGBTNWKR5MJMXMWHD67BOE","short_pith_number":"pith:N7JOGGBT","schema_version":"1.0","canonical_sha256":"6fd2e318336d951eb12cbb2c71fbe1712f36889c616586d41eb9057b7c6402fb","source":{"kind":"arxiv","id":"2310.12359","version":2},"attestation_state":"computed","paper":{"title":"MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.MA","authors_text":"Daniel Work, Gautam Biswas, Marcos Quinones-Grueiro, William Barbour, Yanbing Wang, Yuhang Zhang, Zhiyao Zhang","submitted_at":"2023-10-18T22:09:29Z","abstract_excerpt":"Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions. Most of the deployed VSL control algorithms so far are rule-based, lacking generalizability under varying and complex traffic scenarios. In this work, we propose MARVEL (Multi-Agent Reinforcement-learning for large-scale Variable spEed Limits), a novel framework for large-scale VSL control on highway corridors with real-world deployment settings. MARVEL utilizes onl"},"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":"2310.12359","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2023-10-18T22:09:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"30d07cec683ac4696238c75a7f835bfb45182bbd9d9b88d25a24c897987450da","abstract_canon_sha256":"acd8807debfb6fa4b1afb96ff8150f9874aad2c91881c41f592a2c44be34f46c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:55.220977Z","signature_b64":"le0x5m9FKJn9yzzhyqXBnuIolU9R+RLst4rvi1i5Og/NQ3vOK+60hz6LemqhnTjhlicGTPZjAuCHuir6q9u6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6fd2e318336d951eb12cbb2c71fbe1712f36889c616586d41eb9057b7c6402fb","last_reissued_at":"2026-07-05T07:56:55.220461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:55.220461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.MA","authors_text":"Daniel Work, Gautam Biswas, Marcos Quinones-Grueiro, William Barbour, Yanbing Wang, Yuhang Zhang, Zhiyao Zhang","submitted_at":"2023-10-18T22:09:29Z","abstract_excerpt":"Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions. Most of the deployed VSL control algorithms so far are rule-based, lacking generalizability under varying and complex traffic scenarios. In this work, we propose MARVEL (Multi-Agent Reinforcement-learning for large-scale Variable spEed Limits), a novel framework for large-scale VSL control on highway corridors with real-world deployment settings. MARVEL utilizes onl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.12359","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/2310.12359/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":"2310.12359","created_at":"2026-07-05T07:56:55.220522+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.12359v2","created_at":"2026-07-05T07:56:55.220522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.12359","created_at":"2026-07-05T07:56:55.220522+00:00"},{"alias_kind":"pith_short_12","alias_value":"N7JOGGBTNWKR","created_at":"2026-07-05T07:56:55.220522+00:00"},{"alias_kind":"pith_short_16","alias_value":"N7JOGGBTNWKR5MJM","created_at":"2026-07-05T07:56:55.220522+00:00"},{"alias_kind":"pith_short_8","alias_value":"N7JOGGBT","created_at":"2026-07-05T07:56:55.220522+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09530","citing_title":"Universal Scaling Laws in Freeway Traffic","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE","json":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE.json","graph_json":"https://pith.science/api/pith-number/N7JOGGBTNWKR5MJMXMWHD67BOE/graph.json","events_json":"https://pith.science/api/pith-number/N7JOGGBTNWKR5MJMXMWHD67BOE/events.json","paper":"https://pith.science/paper/N7JOGGBT"},"agent_actions":{"view_html":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE","download_json":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE.json","view_paper":"https://pith.science/paper/N7JOGGBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.12359&json=true","fetch_graph":"https://pith.science/api/pith-number/N7JOGGBTNWKR5MJMXMWHD67BOE/graph.json","fetch_events":"https://pith.science/api/pith-number/N7JOGGBTNWKR5MJMXMWHD67BOE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE/action/storage_attestation","attest_author":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE/action/author_attestation","sign_citation":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE/action/citation_signature","submit_replication":"https://pith.science/pith/N7JOGGBTNWKR5MJMXMWHD67BOE/action/replication_record"}},"created_at":"2026-07-05T07:56:55.220522+00:00","updated_at":"2026-07-05T07:56:55.220522+00:00"}