{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SCXESWWMMWU7IPTA6XFK5NW6UU","short_pith_number":"pith:SCXESWWM","schema_version":"1.0","canonical_sha256":"90ae495acc65a9f43e60f5caaeb6dea51b20f6b9fbe19d2cc10c03ee38fbef6b","source":{"kind":"arxiv","id":"2403.03101","version":3},"attestation_state":"computed","paper":{"title":"KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG","cs.MA"],"primary_cat":"cs.CL","authors_text":"Huajun Chen, Jinjie Gu, Lei Liang, Ningyu Zhang, Shiwei Lyu, Shumin Deng, Shuofei Qiao, Yixin Ou, Yue Shen, Yuqi Zhu","submitted_at":"2024-03-05T16:39:12Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions. This inadequacy primarily stems from the lack of built-in action knowledge in language agents, which fails to effectively guide the planning trajectories during task solving and results in planning hallucination. To address this issue, we introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit ac"},"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":"2403.03101","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-05T16:39:12Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG","cs.MA"],"title_canon_sha256":"81e236b32fbc04a5baf9f8e432bc31b8960b1bcba800d7208a2ce22ea36eb5af","abstract_canon_sha256":"c05cdd66295a36035b2d9f59518364330cda8b5d35e335b0556a284b3a75c4a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:07.888768Z","signature_b64":"WpIlpeGLrhZk7nlZS4WVVTAYeZhNrGpnfTIe7Nwi4t+RWco3gy5dAR6U6f5Frqy39BbVdPMc3HGOrMuEfAM8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90ae495acc65a9f43e60f5caaeb6dea51b20f6b9fbe19d2cc10c03ee38fbef6b","last_reissued_at":"2026-07-05T10:18:07.888280Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:07.888280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG","cs.MA"],"primary_cat":"cs.CL","authors_text":"Huajun Chen, Jinjie Gu, Lei Liang, Ningyu Zhang, Shiwei Lyu, Shumin Deng, Shuofei Qiao, Yixin Ou, Yue Shen, Yuqi Zhu","submitted_at":"2024-03-05T16:39:12Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions. This inadequacy primarily stems from the lack of built-in action knowledge in language agents, which fails to effectively guide the planning trajectories during task solving and results in planning hallucination. To address this issue, we introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03101","kind":"arxiv","version":3},"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/2403.03101/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":"2403.03101","created_at":"2026-07-05T10:18:07.888340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03101v3","created_at":"2026-07-05T10:18:07.888340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03101","created_at":"2026-07-05T10:18:07.888340+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCXESWWMMWU7","created_at":"2026-07-05T10:18:07.888340+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCXESWWMMWU7IPTA","created_at":"2026-07-05T10:18:07.888340+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCXESWWM","created_at":"2026-07-05T10:18:07.888340+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21740","citing_title":"Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2606.17628","citing_title":"OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation","ref_index":181,"is_internal_anchor":false},{"citing_arxiv_id":"2503.21460","citing_title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","ref_index":83,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":133,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":132,"is_internal_anchor":false},{"citing_arxiv_id":"2504.19678","citing_title":"From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU","json":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU.json","graph_json":"https://pith.science/api/pith-number/SCXESWWMMWU7IPTA6XFK5NW6UU/graph.json","events_json":"https://pith.science/api/pith-number/SCXESWWMMWU7IPTA6XFK5NW6UU/events.json","paper":"https://pith.science/paper/SCXESWWM"},"agent_actions":{"view_html":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU","download_json":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU.json","view_paper":"https://pith.science/paper/SCXESWWM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03101&json=true","fetch_graph":"https://pith.science/api/pith-number/SCXESWWMMWU7IPTA6XFK5NW6UU/graph.json","fetch_events":"https://pith.science/api/pith-number/SCXESWWMMWU7IPTA6XFK5NW6UU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU/action/storage_attestation","attest_author":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU/action/author_attestation","sign_citation":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU/action/citation_signature","submit_replication":"https://pith.science/pith/SCXESWWMMWU7IPTA6XFK5NW6UU/action/replication_record"}},"created_at":"2026-07-05T10:18:07.888340+00:00","updated_at":"2026-07-05T10:18:07.888340+00:00"}