{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZKNQ3P5DGYR5G35DGMULQ3D4SA","short_pith_number":"pith:ZKNQ3P5D","schema_version":"1.0","canonical_sha256":"ca9b0dbfa33623d36fa33328b86c7c9027eac127f2c665c0f083332d5b396640","source":{"kind":"arxiv","id":"2402.07204","version":5},"attestation_state":"computed","paper":{"title":"ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Ao Qu, Dingyi Zhuang, Han Zheng, Jinhua Zhao, Jushi Kai, Kebing Hou, Tiange Luo, Wei Ma, Xiaotong Guo, Yihao Yan, Yihong Tang, Zhan Zhao, Zhaofeng Wu, Zhaokai Wang","submitted_at":"2024-02-11T13:30:53Z","abstract_excerpt":"Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs)"},"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":"2402.07204","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-02-11T13:30:53Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"bc580aa3a7f6f4cca09169b2c7857f024006fe3b05538fddef8926b51c867df1","abstract_canon_sha256":"1fe3f78a45e9ad5441a8056095575c1f4e4b0c943fe25f576bef9e790cd0f800"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:40.794528Z","signature_b64":"ytcHkJX2AjF9b73p6WOt6cWmVEJquBV3b2XOVaFGLy8YQOxdSzaZ8sswrQ1rznlMmS9dYpqOAEFSD0ke4n3YBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca9b0dbfa33623d36fa33328b86c7c9027eac127f2c665c0f083332d5b396640","last_reissued_at":"2026-07-05T09:58:40.793993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:40.793993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Ao Qu, Dingyi Zhuang, Han Zheng, Jinhua Zhao, Jushi Kai, Kebing Hou, Tiange Luo, Wei Ma, Xiaotong Guo, Yihao Yan, Yihong Tang, Zhan Zhao, Zhaofeng Wu, Zhaokai Wang","submitted_at":"2024-02-11T13:30:53Z","abstract_excerpt":"Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07204","kind":"arxiv","version":5},"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/2402.07204/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":"2402.07204","created_at":"2026-07-05T09:58:40.794054+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.07204v5","created_at":"2026-07-05T09:58:40.794054+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07204","created_at":"2026-07-05T09:58:40.794054+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKNQ3P5DGYR5","created_at":"2026-07-05T09:58:40.794054+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKNQ3P5DGYR5G35D","created_at":"2026-07-05T09:58:40.794054+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKNQ3P5D","created_at":"2026-07-05T09:58:40.794054+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2412.13682","citing_title":"ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00276","citing_title":"Agentic AI for Trip Planning Optimization Application","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA","json":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA.json","graph_json":"https://pith.science/api/pith-number/ZKNQ3P5DGYR5G35DGMULQ3D4SA/graph.json","events_json":"https://pith.science/api/pith-number/ZKNQ3P5DGYR5G35DGMULQ3D4SA/events.json","paper":"https://pith.science/paper/ZKNQ3P5D"},"agent_actions":{"view_html":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA","download_json":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA.json","view_paper":"https://pith.science/paper/ZKNQ3P5D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.07204&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKNQ3P5DGYR5G35DGMULQ3D4SA/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKNQ3P5DGYR5G35DGMULQ3D4SA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA/action/storage_attestation","attest_author":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA/action/author_attestation","sign_citation":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA/action/citation_signature","submit_replication":"https://pith.science/pith/ZKNQ3P5DGYR5G35DGMULQ3D4SA/action/replication_record"}},"created_at":"2026-07-05T09:58:40.794054+00:00","updated_at":"2026-07-05T09:58:40.794054+00:00"}