{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:J4TNIWGQJUJT5NQLYAPRP63BR7","short_pith_number":"pith:J4TNIWGQ","canonical_record":{"source":{"id":"2411.06601","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-10T21:26:17Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"9a01edee15c6990f00b373a9b216b8c7c2e0dcc30d20042e05519e4bac09962a","abstract_canon_sha256":"a489892841f31fa4f504000fa525c8db64d4e147e2ec4de76a9a7214e300097e"},"schema_version":"1.0"},"canonical_sha256":"4f26d458d04d133eb60bc01f17fb618fe7ef32abe7da0dc95608d8f16b57ba66","source":{"kind":"arxiv","id":"2411.06601","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.06601","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"arxiv_version","alias_value":"2411.06601v3","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06601","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_12","alias_value":"J4TNIWGQJUJT","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_16","alias_value":"J4TNIWGQJUJT5NQL","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_8","alias_value":"J4TNIWGQ","created_at":"2026-07-05T10:33:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:J4TNIWGQJUJT5NQLYAPRP63BR7","target":"record","payload":{"canonical_record":{"source":{"id":"2411.06601","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-10T21:26:17Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"9a01edee15c6990f00b373a9b216b8c7c2e0dcc30d20042e05519e4bac09962a","abstract_canon_sha256":"a489892841f31fa4f504000fa525c8db64d4e147e2ec4de76a9a7214e300097e"},"schema_version":"1.0"},"canonical_sha256":"4f26d458d04d133eb60bc01f17fb618fe7ef32abe7da0dc95608d8f16b57ba66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:33:06.510442Z","signature_b64":"VHloJ2y5ID5F+r8Vh+F6AyMLzvl4u/JnY4AO8kXl0pHnEEAiuv92AHiqYGUGBO9rU+qiUWzYEMSxImHLdbDICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f26d458d04d133eb60bc01f17fb618fe7ef32abe7da0dc95608d8f16b57ba66","last_reissued_at":"2026-07-05T10:33:06.509873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:33:06.509873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.06601","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:33:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1H564aCYfe/ji0/rdqEqyyDxfnF5h7r6OdeV161KlrE9jq2vrXOfD955n/7pX+9lhTdfaIhaQ/aLYuC9aT6iDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:12:23.442265Z"},"content_sha256":"e9cc4b182de2e278e56ec5837a74f97798d4810753477f2be3ebed9b2bb1c8d4","schema_version":"1.0","event_id":"sha256:e9cc4b182de2e278e56ec5837a74f97798d4810753477f2be3ebed9b2bb1c8d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:J4TNIWGQJUJT5NQLYAPRP63BR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Rohit Bokade, Xiaoning Jin","submitted_at":"2024-11-10T21:26:17Z","abstract_excerpt":"Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but online MARL requires extensive interactions with the environment, making it costly and impractical. Offline MARL mitigates these challenges by using historical traffic data for training but faces significant difficulties with heterogeneous behavior policies in real-world datasets, where mixed-quality data complicates learning. We introduce OffLight, a novel offline MARL framewor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06601","kind":"arxiv","version":3},"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/2411.06601/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":"2026-07-05T10:33:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xONZrAciLOOOTT65nEHCVT6fCZbnjU4KgpTmRssbWZxmqClPBt33L9yFJqIO9dcKRKXaqoO9rUbX5FoHhElFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:12:23.442991Z"},"content_sha256":"1773a1392013d3d283ba116e37e784e4807462cbe18f0f3c5c7201fb8ff73fec","schema_version":"1.0","event_id":"sha256:1773a1392013d3d283ba116e37e784e4807462cbe18f0f3c5c7201fb8ff73fec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/bundle.json","state_url":"https://pith.science/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/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":"2026-08-18T12:12:23Z","links":{"resolver":"https://pith.science/pith/J4TNIWGQJUJT5NQLYAPRP63BR7","bundle":"https://pith.science/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/bundle.json","state":"https://pith.science/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J4TNIWGQJUJT5NQLYAPRP63BR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:J4TNIWGQJUJT5NQLYAPRP63BR7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a489892841f31fa4f504000fa525c8db64d4e147e2ec4de76a9a7214e300097e","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-10T21:26:17Z","title_canon_sha256":"9a01edee15c6990f00b373a9b216b8c7c2e0dcc30d20042e05519e4bac09962a"},"schema_version":"1.0","source":{"id":"2411.06601","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.06601","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"arxiv_version","alias_value":"2411.06601v3","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06601","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_12","alias_value":"J4TNIWGQJUJT","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_16","alias_value":"J4TNIWGQJUJT5NQL","created_at":"2026-07-05T10:33:06Z"},{"alias_kind":"pith_short_8","alias_value":"J4TNIWGQ","created_at":"2026-07-05T10:33:06Z"}],"graph_snapshots":[{"event_id":"sha256:1773a1392013d3d283ba116e37e784e4807462cbe18f0f3c5c7201fb8ff73fec","target":"graph","created_at":"2026-07-05T10:33:06Z","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/2411.06601/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but online MARL requires extensive interactions with the environment, making it costly and impractical. Offline MARL mitigates these challenges by using historical traffic data for training but faces significant difficulties with heterogeneous behavior policies in real-world datasets, where mixed-quality data complicates learning. We introduce OffLight, a novel offline MARL framewor","authors_text":"Rohit Bokade, Xiaoning Jin","cross_cats":["cs.LG","cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-10T21:26:17Z","title":"OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06601","kind":"arxiv","version":3},"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:e9cc4b182de2e278e56ec5837a74f97798d4810753477f2be3ebed9b2bb1c8d4","target":"record","created_at":"2026-07-05T10:33:06Z","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":"a489892841f31fa4f504000fa525c8db64d4e147e2ec4de76a9a7214e300097e","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-10T21:26:17Z","title_canon_sha256":"9a01edee15c6990f00b373a9b216b8c7c2e0dcc30d20042e05519e4bac09962a"},"schema_version":"1.0","source":{"id":"2411.06601","kind":"arxiv","version":3}},"canonical_sha256":"4f26d458d04d133eb60bc01f17fb618fe7ef32abe7da0dc95608d8f16b57ba66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f26d458d04d133eb60bc01f17fb618fe7ef32abe7da0dc95608d8f16b57ba66","first_computed_at":"2026-07-05T10:33:06.509873Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:33:06.509873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VHloJ2y5ID5F+r8Vh+F6AyMLzvl4u/JnY4AO8kXl0pHnEEAiuv92AHiqYGUGBO9rU+qiUWzYEMSxImHLdbDICQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:33:06.510442Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.06601","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e9cc4b182de2e278e56ec5837a74f97798d4810753477f2be3ebed9b2bb1c8d4","sha256:1773a1392013d3d283ba116e37e784e4807462cbe18f0f3c5c7201fb8ff73fec"],"state_sha256":"426c9dbac153136ef06d7147a23946eb313f5cd5a30d26cade64b477c85d4c0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ROX4KyKVTckGBrIhdXbY4wd3pTdpBASJ+krMJx7sbH+LYIpVd8cEbzQLcDJCp4D3JojYaLYu5L4JY/OId05QBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:12:23.449245Z","bundle_sha256":"7688c685f2d39e40830c9e4ba3b328eab0b85964889aee91dfe604d2da56002e"}}