{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7F3MF5U4GEAEEXQS72LZJU3C5G","short_pith_number":"pith:7F3MF5U4","schema_version":"1.0","canonical_sha256":"f976c2f69c3100425e12fe9794d362e99cefb700aa94d7ee4b7a5e8f2a5e8a52","source":{"kind":"arxiv","id":"2601.10609","version":5},"attestation_state":"computed","paper":{"title":"iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hongyu Zhang, Hua Ma, Yunshan Ma, Zhuoxuan Huang, Zhu Sun","submitted_at":"2026-01-15T17:24:51Z","abstract_excerpt":"Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb"},"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":"2601.10609","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-01-15T17:24:51Z","cross_cats_sorted":[],"title_canon_sha256":"9c0177393eaa443bf1b5c2e679189c43c7ac6a013757a7fb54664838852f40e1","abstract_canon_sha256":"b16927e8ca2173565737236c9760e32dd97b55e1bed07bb9486200ae3ae6e86f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T01:20:53.527230Z","signature_b64":"YYxRDEVstNrLJkqiHbSLcJ0eB0fShgGd2KSsFg+UBY2XdcOCFdKUe7/pj7R8Ejcqn+dF+VBgnWK+0mJQkf+DBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f976c2f69c3100425e12fe9794d362e99cefb700aa94d7ee4b7a5e8f2a5e8a52","last_reissued_at":"2026-07-15T01:20:53.526292Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T01:20:53.526292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hongyu Zhang, Hua Ma, Yunshan Ma, Zhuoxuan Huang, Zhu Sun","submitted_at":"2026-01-15T17:24:51Z","abstract_excerpt":"Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10609","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/2601.10609/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":"2601.10609","created_at":"2026-07-15T01:20:53.526689+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.10609v5","created_at":"2026-07-15T01:20:53.526689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10609","created_at":"2026-07-15T01:20:53.526689+00:00"},{"alias_kind":"pith_short_12","alias_value":"7F3MF5U4GEAE","created_at":"2026-07-15T01:20:53.526689+00:00"},{"alias_kind":"pith_short_16","alias_value":"7F3MF5U4GEAEEXQS","created_at":"2026-07-15T01:20:53.526689+00:00"},{"alias_kind":"pith_short_8","alias_value":"7F3MF5U4","created_at":"2026-07-15T01:20:53.526689+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.16565","citing_title":"AlterAtlas: Shifting Travel Planning from AI Generation to Validation via Persona-Driven Simulations","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G","json":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G.json","graph_json":"https://pith.science/api/pith-number/7F3MF5U4GEAEEXQS72LZJU3C5G/graph.json","events_json":"https://pith.science/api/pith-number/7F3MF5U4GEAEEXQS72LZJU3C5G/events.json","paper":"https://pith.science/paper/7F3MF5U4"},"agent_actions":{"view_html":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G","download_json":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G.json","view_paper":"https://pith.science/paper/7F3MF5U4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.10609&json=true","fetch_graph":"https://pith.science/api/pith-number/7F3MF5U4GEAEEXQS72LZJU3C5G/graph.json","fetch_events":"https://pith.science/api/pith-number/7F3MF5U4GEAEEXQS72LZJU3C5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G/action/storage_attestation","attest_author":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G/action/author_attestation","sign_citation":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G/action/citation_signature","submit_replication":"https://pith.science/pith/7F3MF5U4GEAEEXQS72LZJU3C5G/action/replication_record"}},"created_at":"2026-07-15T01:20:53.526689+00:00","updated_at":"2026-07-15T01:20:53.526689+00:00"}