{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XL7OXLEL73Q4UFWBLR3GWYWU2D","short_pith_number":"pith:XL7OXLEL","schema_version":"1.0","canonical_sha256":"bafeebac8bfee1ca16c15c766b62d4d0ed78c3a7baab9226e7a0d1742711207c","source":{"kind":"arxiv","id":"2402.14744","version":3},"attestation_state":"computed","paper":{"title":"Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CY","cs.LG"],"primary_cat":"cs.AI","authors_text":"Chuang Yang, Chuan Xiao, Jiawei Wang, Makoto Onizuka, Noboru Koshizuka, Renhe Jiang, Ryosuke Shibasaki, Zengqing Wu","submitted_at":"2024-02-22T18:03:14Z","abstract_excerpt":"This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual "},"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":"2402.14744","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-22T18:03:14Z","cross_cats_sorted":["cs.CL","cs.CY","cs.LG"],"title_canon_sha256":"380be5f462409867b38a7757570f94c8ccf55efed9a92825403fcd3fc54238c9","abstract_canon_sha256":"b4903dd443111e652775cd6827a12cef838bd172b6d5edb54c8d54dcdf9db282"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:24.459344Z","signature_b64":"EAYS1sYS5B83MlDALJrzjoqLz4ExMgmjunjGGV1j4KjLghQL4eAU0OSEIYEtiT1Rk/Xj5b7l1Mkz8EEWsCHaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bafeebac8bfee1ca16c15c766b62d4d0ed78c3a7baab9226e7a0d1742711207c","last_reissued_at":"2026-07-05T09:26:24.458849Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:24.458849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CY","cs.LG"],"primary_cat":"cs.AI","authors_text":"Chuang Yang, Chuan Xiao, Jiawei Wang, Makoto Onizuka, Noboru Koshizuka, Renhe Jiang, Ryosuke Shibasaki, Zengqing Wu","submitted_at":"2024-02-22T18:03:14Z","abstract_excerpt":"This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14744","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/2402.14744/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":"2402.14744","created_at":"2026-07-05T09:26:24.458906+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.14744v3","created_at":"2026-07-05T09:26:24.458906+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14744","created_at":"2026-07-05T09:26:24.458906+00:00"},{"alias_kind":"pith_short_12","alias_value":"XL7OXLEL73Q4","created_at":"2026-07-05T09:26:24.458906+00:00"},{"alias_kind":"pith_short_16","alias_value":"XL7OXLEL73Q4UFWB","created_at":"2026-07-05T09:26:24.458906+00:00"},{"alias_kind":"pith_short_8","alias_value":"XL7OXLEL","created_at":"2026-07-05T09:26:24.458906+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12657","citing_title":"TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00991","citing_title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","ref_index":62,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17419","citing_title":"ARMove: Learning to Predict Human Mobility through Agentic Reasoning","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D","json":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D.json","graph_json":"https://pith.science/api/pith-number/XL7OXLEL73Q4UFWBLR3GWYWU2D/graph.json","events_json":"https://pith.science/api/pith-number/XL7OXLEL73Q4UFWBLR3GWYWU2D/events.json","paper":"https://pith.science/paper/XL7OXLEL"},"agent_actions":{"view_html":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D","download_json":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D.json","view_paper":"https://pith.science/paper/XL7OXLEL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.14744&json=true","fetch_graph":"https://pith.science/api/pith-number/XL7OXLEL73Q4UFWBLR3GWYWU2D/graph.json","fetch_events":"https://pith.science/api/pith-number/XL7OXLEL73Q4UFWBLR3GWYWU2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D/action/storage_attestation","attest_author":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D/action/author_attestation","sign_citation":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D/action/citation_signature","submit_replication":"https://pith.science/pith/XL7OXLEL73Q4UFWBLR3GWYWU2D/action/replication_record"}},"created_at":"2026-07-05T09:26:24.458906+00:00","updated_at":"2026-07-05T09:26:24.458906+00:00"}