{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JIJZO3BITCISRFASCTPWGNSQQN","short_pith_number":"pith:JIJZO3BI","schema_version":"1.0","canonical_sha256":"4a13976c28989128941214df6336508378653a8a4102355b4c06c11fafee905f","source":{"kind":"arxiv","id":"2411.11581","version":5},"attestation_state":"computed","paper":{"title":"OASIS: Open Agent Social Interaction Simulations with One Million Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bernard Ghanem, Bowen Dong, Chaochao Lu, Guohao Li, Huchuan Lu, Jing Shao, Jinsong Chen, Lijun Wang, Martz Ma, Philip Torr, Prateek Gupta, Shuyue Hu, Wanli Ouyang, Xu Jia, Yu Qiao, Yuxian Jiang, Zaibin Zhang, Zhenfei Yin, Zhiyu Wang, Zijian Ling, Zirui Zheng, Ziyi Yang, Ziyue Gan","submitted_at":"2024-11-18T13:57:35Z","abstract_excerpt":"There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world s"},"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":"2411.11581","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-18T13:57:35Z","cross_cats_sorted":[],"title_canon_sha256":"a2ca4cd324374635964d9e27caa26cb0e7b9b099377894bbacf4dfc2d23acee2","abstract_canon_sha256":"257eac8f6ce98c5928508769e1a862b360298f4ba188d3bb42e569ecaf4f328f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:37.450709Z","signature_b64":"XLiRbofDGI5Zd/2vhYObIDIbvf3/upR4GN4hqrmddDnfV4qRz3GHSsSJwZ1PBarSpyw6hBRST9sa0TFFKeevAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a13976c28989128941214df6336508378653a8a4102355b4c06c11fafee905f","last_reissued_at":"2026-07-05T10:37:37.449639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:37.449639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OASIS: Open Agent Social Interaction Simulations with One Million Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bernard Ghanem, Bowen Dong, Chaochao Lu, Guohao Li, Huchuan Lu, Jing Shao, Jinsong Chen, Lijun Wang, Martz Ma, Philip Torr, Prateek Gupta, Shuyue Hu, Wanli Ouyang, Xu Jia, Yu Qiao, Yuxian Jiang, Zaibin Zhang, Zhenfei Yin, Zhiyu Wang, Zijian Ling, Zirui Zheng, Ziyi Yang, Ziyue Gan","submitted_at":"2024-11-18T13:57:35Z","abstract_excerpt":"There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11581","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/2411.11581/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":"2411.11581","created_at":"2026-07-05T10:37:37.449770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.11581v5","created_at":"2026-07-05T10:37:37.449770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11581","created_at":"2026-07-05T10:37:37.449770+00:00"},{"alias_kind":"pith_short_12","alias_value":"JIJZO3BITCIS","created_at":"2026-07-05T10:37:37.449770+00:00"},{"alias_kind":"pith_short_16","alias_value":"JIJZO3BITCISRFAS","created_at":"2026-07-05T10:37:37.449770+00:00"},{"alias_kind":"pith_short_8","alias_value":"JIJZO3BI","created_at":"2026-07-05T10:37:37.449770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":35,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.06080","citing_title":"From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2604.18011","citing_title":"Topology-Aware LLM-Driven Social Simulation: A Unified Framework for Efficient and Realistic Agent Dynamics","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2606.12369","citing_title":"Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11482","citing_title":"Building Social World Models with Large Language Models","ref_index":126,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03137","citing_title":"Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06971","citing_title":"Modeling U.S. Attitudes Toward China via an Event-Steered Multi-Agent Simulator","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06399","citing_title":"CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments","ref_index":97,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02437","citing_title":"On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27826","citing_title":"NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30801","citing_title":"Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale","ref_index":88,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26883","citing_title":"EconSimulacra: A Digital Twin Platform of Socio-Economic Systems Powered by LLM Agents","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25680","citing_title":"Simulating Human Memory with Language Models","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27584","citing_title":"Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention","ref_index":216,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28598","citing_title":"Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27539","citing_title":"Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30258","citing_title":"EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations","ref_index":13,"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":109,"is_internal_anchor":false},{"citing_arxiv_id":"2507.11521","citing_title":"Opinion dynamics: Statistical physics and beyond","ref_index":291,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17079","citing_title":"Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18890","citing_title":"Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits","ref_index":77,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19915","citing_title":"LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2507.09788","citing_title":"TinyTroupe: An LLM-powered Multiagent Persona Simulation Toolkit","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09321","citing_title":"OpenIIR: An Open Simulation Platform for Information Retrieval Research","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12512","citing_title":"Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13725","citing_title":"ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN","json":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN.json","graph_json":"https://pith.science/api/pith-number/JIJZO3BITCISRFASCTPWGNSQQN/graph.json","events_json":"https://pith.science/api/pith-number/JIJZO3BITCISRFASCTPWGNSQQN/events.json","paper":"https://pith.science/paper/JIJZO3BI"},"agent_actions":{"view_html":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN","download_json":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN.json","view_paper":"https://pith.science/paper/JIJZO3BI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.11581&json=true","fetch_graph":"https://pith.science/api/pith-number/JIJZO3BITCISRFASCTPWGNSQQN/graph.json","fetch_events":"https://pith.science/api/pith-number/JIJZO3BITCISRFASCTPWGNSQQN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN/action/storage_attestation","attest_author":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN/action/author_attestation","sign_citation":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN/action/citation_signature","submit_replication":"https://pith.science/pith/JIJZO3BITCISRFASCTPWGNSQQN/action/replication_record"}},"created_at":"2026-07-05T10:37:37.449770+00:00","updated_at":"2026-07-05T10:37:37.449770+00:00"}