{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W6UPUW4OG5ZKXYL33OMIDUQEYG","short_pith_number":"pith:W6UPUW4O","schema_version":"1.0","canonical_sha256":"b7a8fa5b8e3772abe17bdb9881d204c1993311a8fe229cd3f7671091540a3507","source":{"kind":"arxiv","id":"2410.21286","version":1},"attestation_state":"computed","paper":{"title":"OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Fengli Xu, Jie Feng, Jingzhe Yuan, Jun Zhang, Qingbin Zeng, Yong Li, Yuwei Yan, Zhiheng Zheng","submitted_at":"2024-10-11T13:52:35Z","abstract_excerpt":"Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has led to the development of LLM agents capable of simulating urban activities with unprecedented realism. However, the extreme high computational cost of LLMs presents significant challenges for scaling up the simulations of LLM agents. To address this problem, we propose OpenCity, a"},"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":"2410.21286","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2024-10-11T13:52:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f9fe7b81d6942d7c6c224259048cd528f351a45fe812a140efc77f6b8e3b2c51","abstract_canon_sha256":"1f78eab182ae1a904e771c44d3755ca0483609dce477d7928f61adfd5b66a051"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:26.108888Z","signature_b64":"RUGyZ+Qz3jXJVSqEKE2ednwgsjskUB+Q8Q24OROTTEjls2Lli7QWEdPn0maX7VlJYFfxsxmueVA447tYU0BkCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7a8fa5b8e3772abe17bdb9881d204c1993311a8fe229cd3f7671091540a3507","last_reissued_at":"2026-07-05T09:27:26.108400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:26.108400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Fengli Xu, Jie Feng, Jingzhe Yuan, Jun Zhang, Qingbin Zeng, Yong Li, Yuwei Yan, Zhiheng Zheng","submitted_at":"2024-10-11T13:52:35Z","abstract_excerpt":"Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has led to the development of LLM agents capable of simulating urban activities with unprecedented realism. However, the extreme high computational cost of LLMs presents significant challenges for scaling up the simulations of LLM agents. To address this problem, we propose OpenCity, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.21286","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/2410.21286/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":"2410.21286","created_at":"2026-07-05T09:27:26.108457+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.21286v1","created_at":"2026-07-05T09:27:26.108457+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.21286","created_at":"2026-07-05T09:27:26.108457+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6UPUW4OG5ZK","created_at":"2026-07-05T09:27:26.108457+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6UPUW4OG5ZKXYL3","created_at":"2026-07-05T09:27:26.108457+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6UPUW4O","created_at":"2026-07-05T09:27:26.108457+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00989","citing_title":"SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03030","citing_title":"Do Matching Mechanisms Work with LLM Agents?","ref_index":167,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27650","citing_title":"GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27766","citing_title":"Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13140","citing_title":"MIDSim: Simulating Multi-Channel Information Diffusion in Social Media with LLM-Powered Multi-Agent System","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2502.08691","citing_title":"AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society","ref_index":108,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07199","citing_title":"Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG","json":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG.json","graph_json":"https://pith.science/api/pith-number/W6UPUW4OG5ZKXYL33OMIDUQEYG/graph.json","events_json":"https://pith.science/api/pith-number/W6UPUW4OG5ZKXYL33OMIDUQEYG/events.json","paper":"https://pith.science/paper/W6UPUW4O"},"agent_actions":{"view_html":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG","download_json":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG.json","view_paper":"https://pith.science/paper/W6UPUW4O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.21286&json=true","fetch_graph":"https://pith.science/api/pith-number/W6UPUW4OG5ZKXYL33OMIDUQEYG/graph.json","fetch_events":"https://pith.science/api/pith-number/W6UPUW4OG5ZKXYL33OMIDUQEYG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG/action/storage_attestation","attest_author":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG/action/author_attestation","sign_citation":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG/action/citation_signature","submit_replication":"https://pith.science/pith/W6UPUW4OG5ZKXYL33OMIDUQEYG/action/replication_record"}},"created_at":"2026-07-05T09:27:26.108457+00:00","updated_at":"2026-07-05T09:27:26.108457+00:00"}