{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EBD6QROMVTK4ETDOOFWWGN637D","short_pith_number":"pith:EBD6QROM","schema_version":"1.0","canonical_sha256":"2047e845ccacd5c24c6e716d6337dbf8c59342d3e3314251760d4725ce8afc16","source":{"kind":"arxiv","id":"2312.17259","version":2},"attestation_state":"computed","paper":{"title":"Empowering Working Memory for Large Language Model Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hang Yang, Jianchuan Qi, Jing Guo, Ming Xu, Nan Li, Ruiqiao Li, Si Zhang, Yuzhen Feng","submitted_at":"2023-12-22T05:59:00Z","abstract_excerpt":"Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture. The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. To address this, an innovative model is proposed incorpor"},"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":"2312.17259","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-22T05:59:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e6816b7c1e030163f5bbf22ee0ec6cfc0a7bba902a8b6df4103a74ca3de73eb8","abstract_canon_sha256":"0a9c8ac150a665d382f7bc3ffdbf3283d66a0beff269878db3b5a1003844d58c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:59.365540Z","signature_b64":"UOUpG06GOuxQKjEQ1kRSajuQNogOUbzpEDr2fj8OO6+NJjbbdx+fpsnXu5lGYvvOyJ3oUJclZRjuZ63PbUBeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2047e845ccacd5c24c6e716d6337dbf8c59342d3e3314251760d4725ce8afc16","last_reissued_at":"2026-07-05T08:23:59.365034Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:59.365034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Empowering Working Memory for Large Language Model Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hang Yang, Jianchuan Qi, Jing Guo, Ming Xu, Nan Li, Ruiqiao Li, Si Zhang, Yuzhen Feng","submitted_at":"2023-12-22T05:59:00Z","abstract_excerpt":"Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture. The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. To address this, an innovative model is proposed incorpor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.17259","kind":"arxiv","version":2},"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/2312.17259/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":"2312.17259","created_at":"2026-07-05T08:23:59.365104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.17259v2","created_at":"2026-07-05T08:23:59.365104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.17259","created_at":"2026-07-05T08:23:59.365104+00:00"},{"alias_kind":"pith_short_12","alias_value":"EBD6QROMVTK4","created_at":"2026-07-05T08:23:59.365104+00:00"},{"alias_kind":"pith_short_16","alias_value":"EBD6QROMVTK4ETDO","created_at":"2026-07-05T08:23:59.365104+00:00"},{"alias_kind":"pith_short_8","alias_value":"EBD6QROM","created_at":"2026-07-05T08:23:59.365104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05690","citing_title":"Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2502.08691","citing_title":"AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27283","citing_title":"Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D","json":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D.json","graph_json":"https://pith.science/api/pith-number/EBD6QROMVTK4ETDOOFWWGN637D/graph.json","events_json":"https://pith.science/api/pith-number/EBD6QROMVTK4ETDOOFWWGN637D/events.json","paper":"https://pith.science/paper/EBD6QROM"},"agent_actions":{"view_html":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D","download_json":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D.json","view_paper":"https://pith.science/paper/EBD6QROM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.17259&json=true","fetch_graph":"https://pith.science/api/pith-number/EBD6QROMVTK4ETDOOFWWGN637D/graph.json","fetch_events":"https://pith.science/api/pith-number/EBD6QROMVTK4ETDOOFWWGN637D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D/action/storage_attestation","attest_author":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D/action/author_attestation","sign_citation":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D/action/citation_signature","submit_replication":"https://pith.science/pith/EBD6QROMVTK4ETDOOFWWGN637D/action/replication_record"}},"created_at":"2026-07-05T08:23:59.365104+00:00","updated_at":"2026-07-05T08:23:59.365104+00:00"}