{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MHEK7OKTB73F6NXPDJIXK7J773","short_pith_number":"pith:MHEK7OKT","schema_version":"1.0","canonical_sha256":"61c8afb9530ff65f36ef1a51757d3ffee1351fa4c072d4abaeed44a2f334c77d","source":{"kind":"arxiv","id":"2506.21805","version":1},"attestation_state":"computed","paper":{"title":"CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Narimasa Watanabe, Nicolas Bougie","submitted_at":"2025-06-26T23:11:42Z","abstract_excerpt":"Modeling human behavior in urban environments is fundamental for social science, behavioral studies, and urban planning. Prior work often rely on rigid, hand-crafted rules, limiting their ability to simulate nuanced intentions, plans, and adaptive behaviors. Addressing these challenges, we envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models. In CitySim, agents generate realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. To e"},"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":"2506.21805","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T23:11:42Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"d38389361cb5ee3050af59e9c3697a2b2f733bf5e7e3288e0ede2750fe6ce1a9","abstract_canon_sha256":"b70ee37f895e80fafa890b88afd09d21673077f62e06f09099eea7fb7ae44840"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:01.543757Z","signature_b64":"SCLeh4Be0D2dss9kWZg0cn7IYJNvlHaYR7k2Ir2aelMsGG2sMp2DNJ3oV9z+jlVlCMr9xMZyW1+S9HbiaH4QBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61c8afb9530ff65f36ef1a51757d3ffee1351fa4c072d4abaeed44a2f334c77d","last_reissued_at":"2026-07-05T11:28:01.543287Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:01.543287Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Narimasa Watanabe, Nicolas Bougie","submitted_at":"2025-06-26T23:11:42Z","abstract_excerpt":"Modeling human behavior in urban environments is fundamental for social science, behavioral studies, and urban planning. Prior work often rely on rigid, hand-crafted rules, limiting their ability to simulate nuanced intentions, plans, and adaptive behaviors. Addressing these challenges, we envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models. In CitySim, agents generate realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. To e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21805","kind":"arxiv","version":1},"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/2506.21805/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":"2506.21805","created_at":"2026-07-05T11:28:01.543352+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21805v1","created_at":"2026-07-05T11:28:01.543352+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21805","created_at":"2026-07-05T11:28:01.543352+00:00"},{"alias_kind":"pith_short_12","alias_value":"MHEK7OKTB73F","created_at":"2026-07-05T11:28:01.543352+00:00"},{"alias_kind":"pith_short_16","alias_value":"MHEK7OKTB73F6NXP","created_at":"2026-07-05T11:28:01.543352+00:00"},{"alias_kind":"pith_short_8","alias_value":"MHEK7OKT","created_at":"2026-07-05T11:28:01.543352+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.17239","citing_title":"Privacy-Preserving Synthetic Dataset of Individual Daily Trajectories for City-Scale Mobility Analytics","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773","json":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773.json","graph_json":"https://pith.science/api/pith-number/MHEK7OKTB73F6NXPDJIXK7J773/graph.json","events_json":"https://pith.science/api/pith-number/MHEK7OKTB73F6NXPDJIXK7J773/events.json","paper":"https://pith.science/paper/MHEK7OKT"},"agent_actions":{"view_html":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773","download_json":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773.json","view_paper":"https://pith.science/paper/MHEK7OKT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21805&json=true","fetch_graph":"https://pith.science/api/pith-number/MHEK7OKTB73F6NXPDJIXK7J773/graph.json","fetch_events":"https://pith.science/api/pith-number/MHEK7OKTB73F6NXPDJIXK7J773/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773/action/storage_attestation","attest_author":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773/action/author_attestation","sign_citation":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773/action/citation_signature","submit_replication":"https://pith.science/pith/MHEK7OKTB73F6NXPDJIXK7J773/action/replication_record"}},"created_at":"2026-07-05T11:28:01.543352+00:00","updated_at":"2026-07-05T11:28:01.543352+00:00"}