{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3GZRVHTDECPLLZC64DCANK5GFO","short_pith_number":"pith:3GZRVHTD","schema_version":"1.0","canonical_sha256":"d9b31a9e63209eb5e45ee0c406aba62bad749432347e2334a938c6d6db76fb64","source":{"kind":"arxiv","id":"2306.06770","version":4},"attestation_state":"computed","paper":{"title":"Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.HC","cs.RO"],"primary_cat":"cs.AI","authors_text":"James R. Kirk, John E. Laird, Peter Lindes, Robert E. Wray","submitted_at":"2023-06-11T20:50:14Z","abstract_excerpt":"Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM, but alone it is insufficient for acquiring relevant, situationally grounded knowledge for an embodied agent learning novel tasks. We describe a cognitive-agent approach, STARS, that extends and complements prompt engineering, mitigating its limitations and thus enabling an agent to acquire new task knowledge matched to its native language capabilities, embodiment, environment, and user preferences. The STARS appro"},"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":"2306.06770","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-11T20:50:14Z","cross_cats_sorted":["cs.HC","cs.RO"],"title_canon_sha256":"af7daf6809a0e10675ab3a37a98d754a5c8f41dc87f786e3d64a26b5251568c6","abstract_canon_sha256":"9f9bfe089d2696d9042e4ac4c419883de879c8cb56ba96e912d1ab525e8765b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:03.100193Z","signature_b64":"6cvaX6L1wNaLmx2fa0bHv1VWS1iXRzQkcS4Z3I9egINrKi+/VZcoIFG2Oj4PTQmXbN9NH93AMXil4E/m9TQsDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9b31a9e63209eb5e45ee0c406aba62bad749432347e2334a938c6d6db76fb64","last_reissued_at":"2026-07-05T07:47:03.099698Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:03.099698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.HC","cs.RO"],"primary_cat":"cs.AI","authors_text":"James R. Kirk, John E. Laird, Peter Lindes, Robert E. Wray","submitted_at":"2023-06-11T20:50:14Z","abstract_excerpt":"Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM, but alone it is insufficient for acquiring relevant, situationally grounded knowledge for an embodied agent learning novel tasks. We describe a cognitive-agent approach, STARS, that extends and complements prompt engineering, mitigating its limitations and thus enabling an agent to acquire new task knowledge matched to its native language capabilities, embodiment, environment, and user preferences. The STARS appro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.06770","kind":"arxiv","version":4},"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/2306.06770/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":"2306.06770","created_at":"2026-07-05T07:47:03.099762+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.06770v4","created_at":"2026-07-05T07:47:03.099762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.06770","created_at":"2026-07-05T07:47:03.099762+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GZRVHTDECPL","created_at":"2026-07-05T07:47:03.099762+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GZRVHTDECPLLZC6","created_at":"2026-07-05T07:47:03.099762+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GZRVHTD","created_at":"2026-07-05T07:47:03.099762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2309.02427","citing_title":"Cognitive Architectures for Language Agents","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO","json":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO.json","graph_json":"https://pith.science/api/pith-number/3GZRVHTDECPLLZC64DCANK5GFO/graph.json","events_json":"https://pith.science/api/pith-number/3GZRVHTDECPLLZC64DCANK5GFO/events.json","paper":"https://pith.science/paper/3GZRVHTD"},"agent_actions":{"view_html":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO","download_json":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO.json","view_paper":"https://pith.science/paper/3GZRVHTD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.06770&json=true","fetch_graph":"https://pith.science/api/pith-number/3GZRVHTDECPLLZC64DCANK5GFO/graph.json","fetch_events":"https://pith.science/api/pith-number/3GZRVHTDECPLLZC64DCANK5GFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO/action/storage_attestation","attest_author":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO/action/author_attestation","sign_citation":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO/action/citation_signature","submit_replication":"https://pith.science/pith/3GZRVHTDECPLLZC64DCANK5GFO/action/replication_record"}},"created_at":"2026-07-05T07:47:03.099762+00:00","updated_at":"2026-07-05T07:47:03.099762+00:00"}