{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X72IT6PLYNPOSZRE64N7O35JLH","short_pith_number":"pith:X72IT6PL","schema_version":"1.0","canonical_sha256":"bff489f9ebc35ee96624f71bf76fa959ea7fa60bde750da63744ade3cbc802bb","source":{"kind":"arxiv","id":"2401.03428","version":1},"attestation_state":"computed","paper":{"title":"Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Ceyao Zhang, Feng Yin, Junhua Zhao, Sirui Hong, Wenhao Li, Xiangrui Meng, Xiuqiang He, Yuheng Cheng, Zekai Wang, Zhengwen Zhang, Zihao Wang","submitted_at":"2024-01-07T09:08:24Z","abstract_excerpt":"Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current resea"},"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":"2401.03428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-01-07T09:08:24Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"5a5d6fccc6f20ba40c0e07e188ec828ee89afbd7d102fb4b2c655a4266d49794","abstract_canon_sha256":"d7a63b1b622c5021a6624836d80b3de870b3406ba09c3a446d7318327ccf4a21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:08.194842Z","signature_b64":"Hci8lHr7XWdGAytymNKroLPmL9TuswsescneNJWoJIqBCh6OiB+5XAXmrIpkaMRQHqMDhSXskZ1j81GwnXGnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bff489f9ebc35ee96624f71bf76fa959ea7fa60bde750da63744ade3cbc802bb","last_reissued_at":"2026-07-05T07:31:08.194373Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:08.194373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Ceyao Zhang, Feng Yin, Junhua Zhao, Sirui Hong, Wenhao Li, Xiangrui Meng, Xiuqiang He, Yuheng Cheng, Zekai Wang, Zhengwen Zhang, Zihao Wang","submitted_at":"2024-01-07T09:08:24Z","abstract_excerpt":"Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current resea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03428","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/2401.03428/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":"2401.03428","created_at":"2026-07-05T07:31:08.194448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.03428v1","created_at":"2026-07-05T07:31:08.194448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03428","created_at":"2026-07-05T07:31:08.194448+00:00"},{"alias_kind":"pith_short_12","alias_value":"X72IT6PLYNPO","created_at":"2026-07-05T07:31:08.194448+00:00"},{"alias_kind":"pith_short_16","alias_value":"X72IT6PLYNPOSZRE","created_at":"2026-07-05T07:31:08.194448+00:00"},{"alias_kind":"pith_short_8","alias_value":"X72IT6PL","created_at":"2026-07-05T07:31:08.194448+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25746","citing_title":"Multi-Agent Coordination Adaptation via Structure-Guided Orchestration","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28077","citing_title":"MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29910","citing_title":"Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2412.02125","citing_title":"Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2503.21460","citing_title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2504.01990","citing_title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","ref_index":258,"is_internal_anchor":false},{"citing_arxiv_id":"2411.18279","citing_title":"Large Language Model-Brained GUI Agents: A Survey","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2509.19185","citing_title":"An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2409.02977","citing_title":"Large Language Model-Based Agents for Software Engineering: A Survey","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2512.10696","citing_title":"Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2411.04468","citing_title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2602.22683","citing_title":"SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses","ref_index":12,"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":78,"is_internal_anchor":false},{"citing_arxiv_id":"2507.13334","citing_title":"A Survey of Context Engineering for Large Language Models","ref_index":175,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17353","citing_title":"Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH","json":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH.json","graph_json":"https://pith.science/api/pith-number/X72IT6PLYNPOSZRE64N7O35JLH/graph.json","events_json":"https://pith.science/api/pith-number/X72IT6PLYNPOSZRE64N7O35JLH/events.json","paper":"https://pith.science/paper/X72IT6PL"},"agent_actions":{"view_html":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH","download_json":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH.json","view_paper":"https://pith.science/paper/X72IT6PL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.03428&json=true","fetch_graph":"https://pith.science/api/pith-number/X72IT6PLYNPOSZRE64N7O35JLH/graph.json","fetch_events":"https://pith.science/api/pith-number/X72IT6PLYNPOSZRE64N7O35JLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH/action/storage_attestation","attest_author":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH/action/author_attestation","sign_citation":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH/action/citation_signature","submit_replication":"https://pith.science/pith/X72IT6PLYNPOSZRE64N7O35JLH/action/replication_record"}},"created_at":"2026-07-05T07:31:08.194448+00:00","updated_at":"2026-07-05T07:31:08.194448+00:00"}