{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WNASY2GVN25GEOWD4JY3VU2R6M","short_pith_number":"pith:WNASY2GV","canonical_record":{"source":{"id":"2608.12256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-08-12T16:57:11Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"dcdde3f49bf54f4a9bc76877b91731425de02a2f2a97fe9b85498bc053f1cdd6","abstract_canon_sha256":"c02ebe3817432232241aafe15f55e988095447643b4bd8be8d6fa23a97a642e3"},"schema_version":"1.0"},"canonical_sha256":"b3412c68d56eba623ac3e271bad351f321e4b778c7846684395ff54be479822a","source":{"kind":"arxiv","id":"2608.12256","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.12256","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"arxiv_version","alias_value":"2608.12256v1","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.12256","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_12","alias_value":"WNASY2GVN25G","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_16","alias_value":"WNASY2GVN25GEOWD","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_8","alias_value":"WNASY2GV","created_at":"2026-08-13T01:30:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WNASY2GVN25GEOWD4JY3VU2R6M","target":"record","payload":{"canonical_record":{"source":{"id":"2608.12256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-08-12T16:57:11Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"dcdde3f49bf54f4a9bc76877b91731425de02a2f2a97fe9b85498bc053f1cdd6","abstract_canon_sha256":"c02ebe3817432232241aafe15f55e988095447643b4bd8be8d6fa23a97a642e3"},"schema_version":"1.0"},"canonical_sha256":"b3412c68d56eba623ac3e271bad351f321e4b778c7846684395ff54be479822a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-13T01:30:38.203050Z","signature_b64":"jPEZ8M6NSYH3CpN0q+ikKKvEJWdtxAdGoJdW34n9z/i6w7ZH0ptPDYnuqTu7VWhUxk9kmJNINs1oCDBOG6UcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3412c68d56eba623ac3e271bad351f321e4b778c7846684395ff54be479822a","last_reissued_at":"2026-08-13T01:30:38.200902Z","signature_status":"signed_v1","first_computed_at":"2026-08-13T01:30:38.200902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.12256","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-13T01:30:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lg+tfVDKyo/MQ/MCHQq3oFJoMOxCWZX2evwc0DuGlC5YbA/20b4UFi2dCVwY+xF4eG7WxflrXPrHVTPeHCTkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T20:46:08.174239Z"},"content_sha256":"2f74dc02fb0b3c57091999633b5afa205bdad4ec2df7060badc40184a8d1089b","schema_version":"1.0","event_id":"sha256:2f74dc02fb0b3c57091999633b5afa205bdad4ec2df7060badc40184a8d1089b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WNASY2GVN25GEOWD4JY3VU2R6M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SelectLight: Learning to Select Signal Plans Generated by Distributed Model Predictive Control for Urban Traffic Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Andrea D'Ariano, Chaopeng Tan, Hong Zhu, Keshuang Tang, Lyuzhou Luo, Zhengyong Gao","submitted_at":"2026-08-12T16:57:11Z","abstract_excerpt":"Coordinated traffic signal control across urban networks must adapt to changing demand while satisfying operational constraints. Multi-objective distributed model predictive control (DMPC) can construct feasible signal plans online, but prescribed rules for selecting among trade-off solutions cannot learn from realized closed-loop outcomes. We propose SelectLight, which implements post-optimization selection by allowing a multi-agent reinforcement learning (MARL) policy to choose directly from plans generated online by DMPC. At each control update, state-pruned multi-objective dynamic programm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.12256","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/2608.12256/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-13T01:30:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BaAh/lc9PAXdacH3CR1+acyemzMxTlfyjRKXVQXX/p+AR/oQin1cF9tCPo6a2FnX/Cc55YZsqyqLKwcMoQrCDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T20:46:08.174830Z"},"content_sha256":"b045716b2e987233b859cdbf2c46e19545e0ab4e07e3cca1e341799fd0fdca11","schema_version":"1.0","event_id":"sha256:b045716b2e987233b859cdbf2c46e19545e0ab4e07e3cca1e341799fd0fdca11"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WNASY2GVN25GEOWD4JY3VU2R6M","target":"integrity","payload":{"note":"Identifier '10.1109/tits.2019' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Transactions on Intelligent Transportation Systems 21, 1086–1095. doi:10.1109/tits.2019. 2901791. Daganzo, C.F.,","arxiv_id":"2608.12256","detector":"doi_compliance","evidence":{"doi":"10.1109/tits.2019","arxiv_id":null,"ref_index":13,"raw_excerpt":"Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Transactions on Intelligent Transportation Systems 21, 1086–1095. doi:10.1109/tits.2019. 2901791. Daganzo, C.F.,","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":13,"audited_at":"2026-08-16T00:28:38.057767Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tits.2019","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"395a3a8801bcc9026dfab4b7d033d0b641f9023dd22c6c14d4abef05a724aa62","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":20149,"payload_sha256":"17faa7a57bac8c999086d369e49e4565243c42fa5d6584d9ab648e69c4113ea7","signature_b64":"/GM2wEGKZmvRcrsOQmvb0oK+ryIZXZsWqbTlWTr1JNLIk6jostffHhquPcVg2Mi4oCEaNweL34v/Hse3PNxyAw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-16T00:33:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LBx9nvpmdKJ1OjDnv1f8kisi8WsN4TKsaUfJHokVpOi+H4cK/SD117zhcIiRG8Dj9sN/jQbPwL7bTXL6tLqfBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T20:46:08.178161Z"},"content_sha256":"7ea01c5b229206d26f1df17e7aa70ae4b00783bb67a3b36c63aa137717d75b4c","schema_version":"1.0","event_id":"sha256:7ea01c5b229206d26f1df17e7aa70ae4b00783bb67a3b36c63aa137717d75b4c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WNASY2GVN25GEOWD4JY3VU2R6M/bundle.json","state_url":"https://pith.science/pith/WNASY2GVN25GEOWD4JY3VU2R6M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WNASY2GVN25GEOWD4JY3VU2R6M/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T20:46:08Z","links":{"resolver":"https://pith.science/pith/WNASY2GVN25GEOWD4JY3VU2R6M","bundle":"https://pith.science/pith/WNASY2GVN25GEOWD4JY3VU2R6M/bundle.json","state":"https://pith.science/pith/WNASY2GVN25GEOWD4JY3VU2R6M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WNASY2GVN25GEOWD4JY3VU2R6M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WNASY2GVN25GEOWD4JY3VU2R6M","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c02ebe3817432232241aafe15f55e988095447643b4bd8be8d6fa23a97a642e3","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-08-12T16:57:11Z","title_canon_sha256":"dcdde3f49bf54f4a9bc76877b91731425de02a2f2a97fe9b85498bc053f1cdd6"},"schema_version":"1.0","source":{"id":"2608.12256","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.12256","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"arxiv_version","alias_value":"2608.12256v1","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.12256","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_12","alias_value":"WNASY2GVN25G","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_16","alias_value":"WNASY2GVN25GEOWD","created_at":"2026-08-13T01:30:38Z"},{"alias_kind":"pith_short_8","alias_value":"WNASY2GV","created_at":"2026-08-13T01:30:38Z"}],"graph_snapshots":[{"event_id":"sha256:b045716b2e987233b859cdbf2c46e19545e0ab4e07e3cca1e341799fd0fdca11","target":"graph","created_at":"2026-08-13T01:30:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2608.12256/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Coordinated traffic signal control across urban networks must adapt to changing demand while satisfying operational constraints. Multi-objective distributed model predictive control (DMPC) can construct feasible signal plans online, but prescribed rules for selecting among trade-off solutions cannot learn from realized closed-loop outcomes. We propose SelectLight, which implements post-optimization selection by allowing a multi-agent reinforcement learning (MARL) policy to choose directly from plans generated online by DMPC. At each control update, state-pruned multi-objective dynamic programm","authors_text":"Andrea D'Ariano, Chaopeng Tan, Hong Zhu, Keshuang Tang, Lyuzhou Luo, Zhengyong Gao","cross_cats":["cs.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-08-12T16:57:11Z","title":"SelectLight: Learning to Select Signal Plans Generated by Distributed Model Predictive Control for Urban Traffic Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.12256","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2f74dc02fb0b3c57091999633b5afa205bdad4ec2df7060badc40184a8d1089b","target":"record","created_at":"2026-08-13T01:30:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c02ebe3817432232241aafe15f55e988095447643b4bd8be8d6fa23a97a642e3","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-08-12T16:57:11Z","title_canon_sha256":"dcdde3f49bf54f4a9bc76877b91731425de02a2f2a97fe9b85498bc053f1cdd6"},"schema_version":"1.0","source":{"id":"2608.12256","kind":"arxiv","version":1}},"canonical_sha256":"b3412c68d56eba623ac3e271bad351f321e4b778c7846684395ff54be479822a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3412c68d56eba623ac3e271bad351f321e4b778c7846684395ff54be479822a","first_computed_at":"2026-08-13T01:30:38.200902Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-13T01:30:38.200902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jPEZ8M6NSYH3CpN0q+ikKKvEJWdtxAdGoJdW34n9z/i6w7ZH0ptPDYnuqTu7VWhUxk9kmJNINs1oCDBOG6UcBw==","signature_status":"signed_v1","signed_at":"2026-08-13T01:30:38.203050Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.12256","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f74dc02fb0b3c57091999633b5afa205bdad4ec2df7060badc40184a8d1089b","sha256:b045716b2e987233b859cdbf2c46e19545e0ab4e07e3cca1e341799fd0fdca11","sha256:7ea01c5b229206d26f1df17e7aa70ae4b00783bb67a3b36c63aa137717d75b4c"],"state_sha256":"c8b108626051c8fc62699ff9c7db20fb417e3dea9374997cafe346aaaeed7354"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tH0NmYn83A/ui75M54SHRJjV31lRvJdj0N5G2KLamwiBLZ2mCItwkRgardfBZvFdvYF+MGxQz4i8plPM/oO6Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T20:46:08.180371Z","bundle_sha256":"888d0b06052b48a4a1120e122966d7727f6f33815cd526afb95b1ad2e031c3cb"}}