{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LZJBWNJQBTOLAIMAEN3W7IPRKE","short_pith_number":"pith:LZJBWNJQ","schema_version":"1.0","canonical_sha256":"5e521b35300cdcb0218023776fa1f151244690e4097e4cf01403a1b391e556f1","source":{"kind":"arxiv","id":"2509.10972","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Computational Cognitive Architectures with LLMs: A Case Study","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ron Sun","submitted_at":"2025-09-13T20:14:14Z","abstract_excerpt":"Computational cognitive architectures are broadly scoped models of the human mind that combine different psychological functionalities (as well as often different computational methods for these different functionalities) into one unified framework. They structure them in a psychologically plausible and validated way. However, such models thus far have only limited computational capabilities, mostly limited by the computational tools and techniques that were adopted. More recently, LLMs have proved to be more capable computationally than any other tools. Thus, in order to deal with both real-w"},"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":"2509.10972","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-09-13T20:14:14Z","cross_cats_sorted":[],"title_canon_sha256":"8ce7f64a59b38a8590faa918289f31d394452519b9f28f6a5a1a20ba5177b98c","abstract_canon_sha256":"db08020c2a4461c47dcfc67ed5bc87c3e6b6caf8507b11ddce7eb114cb906273"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:31.313489Z","signature_b64":"iGKgfjHqDMgi+esapL+fq1PMQLdv/ybczyy8QjC3dfU+TBdndaRHxnsrkNBV8NS/cxvXeP+dt3O3bsOvburDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e521b35300cdcb0218023776fa1f151244690e4097e4cf01403a1b391e556f1","last_reissued_at":"2026-07-05T12:11:31.312975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:31.312975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Computational Cognitive Architectures with LLMs: A Case Study","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ron Sun","submitted_at":"2025-09-13T20:14:14Z","abstract_excerpt":"Computational cognitive architectures are broadly scoped models of the human mind that combine different psychological functionalities (as well as often different computational methods for these different functionalities) into one unified framework. They structure them in a psychologically plausible and validated way. However, such models thus far have only limited computational capabilities, mostly limited by the computational tools and techniques that were adopted. More recently, LLMs have proved to be more capable computationally than any other tools. Thus, in order to deal with both real-w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10972","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/2509.10972/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":"2509.10972","created_at":"2026-07-05T12:11:31.313035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.10972v1","created_at":"2026-07-05T12:11:31.313035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.10972","created_at":"2026-07-05T12:11:31.313035+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZJBWNJQBTOL","created_at":"2026-07-05T12:11:31.313035+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZJBWNJQBTOLAIMA","created_at":"2026-07-05T12:11:31.313035+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZJBWNJQ","created_at":"2026-07-05T12:11:31.313035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE","json":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE.json","graph_json":"https://pith.science/api/pith-number/LZJBWNJQBTOLAIMAEN3W7IPRKE/graph.json","events_json":"https://pith.science/api/pith-number/LZJBWNJQBTOLAIMAEN3W7IPRKE/events.json","paper":"https://pith.science/paper/LZJBWNJQ"},"agent_actions":{"view_html":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE","download_json":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE.json","view_paper":"https://pith.science/paper/LZJBWNJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.10972&json=true","fetch_graph":"https://pith.science/api/pith-number/LZJBWNJQBTOLAIMAEN3W7IPRKE/graph.json","fetch_events":"https://pith.science/api/pith-number/LZJBWNJQBTOLAIMAEN3W7IPRKE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE/action/storage_attestation","attest_author":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE/action/author_attestation","sign_citation":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE/action/citation_signature","submit_replication":"https://pith.science/pith/LZJBWNJQBTOLAIMAEN3W7IPRKE/action/replication_record"}},"created_at":"2026-07-05T12:11:31.313035+00:00","updated_at":"2026-07-05T12:11:31.313035+00:00"}