{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AO5E44QFU5RSQB2BQYU45AB7RQ","short_pith_number":"pith:AO5E44QF","schema_version":"1.0","canonical_sha256":"03ba4e7205a7632807418629ce803f8c039231383ee032809b4dbbd7daf321b3","source":{"kind":"arxiv","id":"2503.11739","version":1},"attestation_state":"computed","paper":{"title":"CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Hao Liu, Siqi Lai, Zirui Yuan","submitted_at":"2025-03-14T15:40:39Z","abstract_excerpt":"Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due to their exceptional problem-solving and generalization capabilities, existing approaches fail to address the essential need for inter-agent coordination, limiting their effectiveness in achieving network-wide optimization. To bridge this gap, we propose CoLLMLight, a cooperative LLM agent framework for TSC. Specifically, we first construct a structured spatiotemporal graph to"},"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":"2503.11739","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-14T15:40:39Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7368fcc67fd3a58a40565b44aa278cb2981954a0c22fd328aa7d53b7f367b6dc","abstract_canon_sha256":"8ac3ae9e5f46016768040c03865e9cc5955593dc1e473b300343979b3d844652"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:42.421266Z","signature_b64":"qqmIt5WViQ0OFX8D3HdTDT6yo+WFu2hEdD8+lzr74nnJsN/bl481OUIrs3FxPAutQaF0jpkVfIxasNDQZgoKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03ba4e7205a7632807418629ce803f8c039231383ee032809b4dbbd7daf321b3","last_reissued_at":"2026-07-05T10:32:42.420591Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:42.420591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Hao Liu, Siqi Lai, Zirui Yuan","submitted_at":"2025-03-14T15:40:39Z","abstract_excerpt":"Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due to their exceptional problem-solving and generalization capabilities, existing approaches fail to address the essential need for inter-agent coordination, limiting their effectiveness in achieving network-wide optimization. To bridge this gap, we propose CoLLMLight, a cooperative LLM agent framework for TSC. Specifically, we first construct a structured spatiotemporal graph to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11739","kind":"arxiv","version":1},"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/2503.11739/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":"2503.11739","created_at":"2026-07-05T10:32:42.420668+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.11739v1","created_at":"2026-07-05T10:32:42.420668+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11739","created_at":"2026-07-05T10:32:42.420668+00:00"},{"alias_kind":"pith_short_12","alias_value":"AO5E44QFU5RS","created_at":"2026-07-05T10:32:42.420668+00:00"},{"alias_kind":"pith_short_16","alias_value":"AO5E44QFU5RSQB2B","created_at":"2026-07-05T10:32:42.420668+00:00"},{"alias_kind":"pith_short_8","alias_value":"AO5E44QF","created_at":"2026-07-05T10:32:42.420668+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":240,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29425","citing_title":"ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":240,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05663","citing_title":"CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17456","citing_title":"TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ","json":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ.json","graph_json":"https://pith.science/api/pith-number/AO5E44QFU5RSQB2BQYU45AB7RQ/graph.json","events_json":"https://pith.science/api/pith-number/AO5E44QFU5RSQB2BQYU45AB7RQ/events.json","paper":"https://pith.science/paper/AO5E44QF"},"agent_actions":{"view_html":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ","download_json":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ.json","view_paper":"https://pith.science/paper/AO5E44QF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.11739&json=true","fetch_graph":"https://pith.science/api/pith-number/AO5E44QFU5RSQB2BQYU45AB7RQ/graph.json","fetch_events":"https://pith.science/api/pith-number/AO5E44QFU5RSQB2BQYU45AB7RQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ/action/storage_attestation","attest_author":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ/action/author_attestation","sign_citation":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ/action/citation_signature","submit_replication":"https://pith.science/pith/AO5E44QFU5RSQB2BQYU45AB7RQ/action/replication_record"}},"created_at":"2026-07-05T10:32:42.420668+00:00","updated_at":"2026-07-05T10:32:42.420668+00:00"}