{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:64F7VDNSUVEHH5B4W27BZPOJSJ","short_pith_number":"pith:64F7VDNS","schema_version":"1.0","canonical_sha256":"f70bfa8db2a54873f43cb6be1cbdc9926b9b42ede090ecd081cddb57a5f33bea","source":{"kind":"arxiv","id":"2310.02172","version":1},"attestation_state":"computed","paper":{"title":"Lyfe Agents: Generative agents for low-cost real-time social interactions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.HC","authors_text":"Andrew Ahn, Guangyu Robert Yang, Jiaxin Ge, Jovana Kondic, Manuel Cortes, Michelangelo Naim, Shuying Luo, Zhao Kaiya","submitted_at":"2023-10-03T16:06:30Z","abstract_excerpt":"Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with real-time responsiveness, all while remaining intelligent and goal-oriented. Key innovations include: (1) an option-action framework, reducing the cost of high-level decisions; (2) asynchronous self-monitoring for better self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing"},"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":"2310.02172","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-10-03T16:06:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5c1afad3e2163974886659c5ea34494297f9b40c34a49aa2c6c1845bd5afa37a","abstract_canon_sha256":"f6999199b77a34ee74d4778d1d6e502ea57322fc07dc4adb3e811a61aaf27d03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:50.920352Z","signature_b64":"t3lPP/+Bx+fjpBT9Xa/WgXyCl8J3BPRqNo1w9QJnbVAKzjFKRzWYZkRiKXPR1r2UBxG5NsKFQZ67xqNuFcUXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f70bfa8db2a54873f43cb6be1cbdc9926b9b42ede090ecd081cddb57a5f33bea","last_reissued_at":"2026-07-05T06:56:50.919876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:50.919876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lyfe Agents: Generative agents for low-cost real-time social interactions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.HC","authors_text":"Andrew Ahn, Guangyu Robert Yang, Jiaxin Ge, Jovana Kondic, Manuel Cortes, Michelangelo Naim, Shuying Luo, Zhao Kaiya","submitted_at":"2023-10-03T16:06:30Z","abstract_excerpt":"Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with real-time responsiveness, all while remaining intelligent and goal-oriented. Key innovations include: (1) an option-action framework, reducing the cost of high-level decisions; (2) asynchronous self-monitoring for better self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02172","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/2310.02172/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":"2310.02172","created_at":"2026-07-05T06:56:50.919926+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.02172v1","created_at":"2026-07-05T06:56:50.919926+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02172","created_at":"2026-07-05T06:56:50.919926+00:00"},{"alias_kind":"pith_short_12","alias_value":"64F7VDNSUVEH","created_at":"2026-07-05T06:56:50.919926+00:00"},{"alias_kind":"pith_short_16","alias_value":"64F7VDNSUVEHH5B4","created_at":"2026-07-05T06:56:50.919926+00:00"},{"alias_kind":"pith_short_8","alias_value":"64F7VDNS","created_at":"2026-07-05T06:56:50.919926+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05557","citing_title":"AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26178","citing_title":"ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12824","citing_title":"Mechanism Plausibility in Generative Agent-Based Modeling","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2404.13501","citing_title":"A Survey on the Memory Mechanism of Large Language Model based Agents","ref_index":148,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12824","citing_title":"Mechanism Plausibility in Generative Agent-Based Modeling","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2402.01680","citing_title":"Large Language Model based Multi-Agents: A Survey of Progress and Challenges","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ","json":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ.json","graph_json":"https://pith.science/api/pith-number/64F7VDNSUVEHH5B4W27BZPOJSJ/graph.json","events_json":"https://pith.science/api/pith-number/64F7VDNSUVEHH5B4W27BZPOJSJ/events.json","paper":"https://pith.science/paper/64F7VDNS"},"agent_actions":{"view_html":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ","download_json":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ.json","view_paper":"https://pith.science/paper/64F7VDNS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.02172&json=true","fetch_graph":"https://pith.science/api/pith-number/64F7VDNSUVEHH5B4W27BZPOJSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/64F7VDNSUVEHH5B4W27BZPOJSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ/action/storage_attestation","attest_author":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ/action/author_attestation","sign_citation":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ/action/citation_signature","submit_replication":"https://pith.science/pith/64F7VDNSUVEHH5B4W27BZPOJSJ/action/replication_record"}},"created_at":"2026-07-05T06:56:50.919926+00:00","updated_at":"2026-07-05T06:56:50.919926+00:00"}