{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Q3HIR5BXLDPN45WMC2Q3SWASX5","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":"5d5837ff16489da33eb234095ec9e19a0a2460f96eb7a06667be4d784003a249","cross_cats_sorted":["cs.AI","cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-06-30T06:42:23Z","title_canon_sha256":"68b4e2b8004c4a4cf0462a5569705738ed37463e13fbaac156a00a26002113aa"},"schema_version":"1.0","source":{"id":"2507.02961","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02961","created_at":"2026-07-02T01:17:12Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02961v2","created_at":"2026-07-02T01:17:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02961","created_at":"2026-07-02T01:17:12Z"},{"alias_kind":"pith_short_12","alias_value":"Q3HIR5BXLDPN","created_at":"2026-07-02T01:17:12Z"},{"alias_kind":"pith_short_16","alias_value":"Q3HIR5BXLDPN45WM","created_at":"2026-07-02T01:17:12Z"},{"alias_kind":"pith_short_8","alias_value":"Q3HIR5BX","created_at":"2026-07-02T01:17:12Z"}],"graph_snapshots":[{"event_id":"sha256:4f435e22b811a9fc2b4c84462115f37ce92a4b44c7f2d6a28e459c714586b26c","target":"graph","created_at":"2026-07-02T01:17:12Z","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/2507.02961/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation. This paper introduces Flow Through Tensors (FTT), a unified computational graph architecture that connects origin destination flows, path probabilities, and link travel times as interconnected tensors. Our framework makes three key contributions: first, it establishes a consistent mathematical structure that enables gradient-based opt","authors_text":"Henan Zhu, Mostafa Ameli, Ram M. Pendyala, Taehooie Kim, Xuesong Zhou, Yudai Honma","cross_cats":["cs.AI","cs.CE"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-06-30T06:42:23Z","title":"Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02961","kind":"arxiv","version":2},"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":"sha256:39a6cdb03899a96e5dc7618e8f9ecdc063330ac1a046d4f36e1049f6b2764584","target":"record","created_at":"2026-07-02T01:17:12Z","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":"5d5837ff16489da33eb234095ec9e19a0a2460f96eb7a06667be4d784003a249","cross_cats_sorted":["cs.AI","cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-06-30T06:42:23Z","title_canon_sha256":"68b4e2b8004c4a4cf0462a5569705738ed37463e13fbaac156a00a26002113aa"},"schema_version":"1.0","source":{"id":"2507.02961","kind":"arxiv","version":2}},"canonical_sha256":"86ce88f43758dede76cc16a1b95812bf6d9494a8b1201a8d94062baf83ee8645","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86ce88f43758dede76cc16a1b95812bf6d9494a8b1201a8d94062baf83ee8645","first_computed_at":"2026-07-02T01:17:12.960393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:17:12.960393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lYFyDHX33QsX+rl1ANkEAEyPeopq5ku6J06suK1X3x8MoA5eNez7hGLhy31+HXr5VpTJtCF53GMzMteY+EUbBA==","signature_status":"signed_v1","signed_at":"2026-07-02T01:17:12.960927Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.02961","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39a6cdb03899a96e5dc7618e8f9ecdc063330ac1a046d4f36e1049f6b2764584","sha256:4f435e22b811a9fc2b4c84462115f37ce92a4b44c7f2d6a28e459c714586b26c"],"state_sha256":"f8d41f5f0349ea8db6c23e1dc2936c0625ce002a4839613336cfd3fc1d3f7b70"}