{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z2METCKDGBL3XWXOH75EIKVJS6","short_pith_number":"pith:Z2METCKD","schema_version":"1.0","canonical_sha256":"ce984989433057bbdaee3ffa442aa9978f8ee880bfd9a98907da83aee69e5177","source":{"kind":"arxiv","id":"2402.12914","version":1},"attestation_state":"computed","paper":{"title":"Large Language Model-based Human-Agent Collaboration for Complex Task Solving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Ji-Rong Wen, Xu Chen, Xueyang Feng, Yankai Lin, Yujia Qin, Zhi-Yuan Chen, Zhiyuan Liu","submitted_at":"2024-02-20T11:03:36Z","abstract_excerpt":"In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. In addition, we propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model design"},"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":"2402.12914","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-20T11:03:36Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"f35fee5cb1cae1ed4e618a181b43baf66136b408f9569d42b0a19dcc23e0d078","abstract_canon_sha256":"e823ff3d9b4d78c045f1581108766c818a0e420daf5b46df0843fd7866132a42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:18.387990Z","signature_b64":"dptOosuq6PeF9MYNXmCl/YYiChKo6CudO/UICMT3sYSeJDxbCP8ns+Oz12sHxl9KIOMnWtcUhW/0xUCkNkOrCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce984989433057bbdaee3ffa442aa9978f8ee880bfd9a98907da83aee69e5177","last_reissued_at":"2026-07-05T07:47:18.387505Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:18.387505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Model-based Human-Agent Collaboration for Complex Task Solving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Ji-Rong Wen, Xu Chen, Xueyang Feng, Yankai Lin, Yujia Qin, Zhi-Yuan Chen, Zhiyuan Liu","submitted_at":"2024-02-20T11:03:36Z","abstract_excerpt":"In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. In addition, we propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model design"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12914","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/2402.12914/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":"2402.12914","created_at":"2026-07-05T07:47:18.387561+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.12914v1","created_at":"2026-07-05T07:47:18.387561+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12914","created_at":"2026-07-05T07:47:18.387561+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2METCKDGBL3","created_at":"2026-07-05T07:47:18.387561+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2METCKDGBL3XWXO","created_at":"2026-07-05T07:47:18.387561+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2METCKD","created_at":"2026-07-05T07:47:18.387561+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2404.11584","citing_title":"The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15607","citing_title":"Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6","json":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6.json","graph_json":"https://pith.science/api/pith-number/Z2METCKDGBL3XWXOH75EIKVJS6/graph.json","events_json":"https://pith.science/api/pith-number/Z2METCKDGBL3XWXOH75EIKVJS6/events.json","paper":"https://pith.science/paper/Z2METCKD"},"agent_actions":{"view_html":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6","download_json":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6.json","view_paper":"https://pith.science/paper/Z2METCKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.12914&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2METCKDGBL3XWXOH75EIKVJS6/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2METCKDGBL3XWXOH75EIKVJS6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6/action/storage_attestation","attest_author":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6/action/author_attestation","sign_citation":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6/action/citation_signature","submit_replication":"https://pith.science/pith/Z2METCKDGBL3XWXOH75EIKVJS6/action/replication_record"}},"created_at":"2026-07-05T07:47:18.387561+00:00","updated_at":"2026-07-05T07:47:18.387561+00:00"}