{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CMTDPJFFBERUDUHBYF73MTRY4I","short_pith_number":"pith:CMTDPJFF","schema_version":"1.0","canonical_sha256":"132637a4a5092341d0e1c17fb64e38e23c478a4cc61a12971e8c7624f7c53d37","source":{"kind":"arxiv","id":"2509.10852","version":1},"attestation_state":"computed","paper":{"title":"Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hyunjong Kim, Sanghwa Kim, Sangyeop Kim, Sungzoon Cho, Yohan Lee","submitted_at":"2025-09-13T15:18:08Z","abstract_excerpt":"Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessi"},"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":"2509.10852","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-13T15:18:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"60c9f61f3d48238731d7f31c3a9c042345ea9e6ee95567cd389dfe63e43393eb","abstract_canon_sha256":"2290f541a6ce2567087b86313ac4e576e94eccbb853e0668ab6b362c6c533522"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:29.227697Z","signature_b64":"uDKcHYXGuDT/tra+t7T+6/VCEfizQQRAvuXAHzanfplr3RXzAnmco9GBprMo/D/NC7kDdlIRRvtxtV/LnzyfDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"132637a4a5092341d0e1c17fb64e38e23c478a4cc61a12971e8c7624f7c53d37","last_reissued_at":"2026-07-05T12:11:29.227161Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:29.227161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hyunjong Kim, Sanghwa Kim, Sangyeop Kim, Sungzoon Cho, Yohan Lee","submitted_at":"2025-09-13T15:18:08Z","abstract_excerpt":"Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10852","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/2509.10852/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":"2509.10852","created_at":"2026-07-05T12:11:29.227226+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.10852v1","created_at":"2026-07-05T12:11:29.227226+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.10852","created_at":"2026-07-05T12:11:29.227226+00:00"},{"alias_kind":"pith_short_12","alias_value":"CMTDPJFFBERU","created_at":"2026-07-05T12:11:29.227226+00:00"},{"alias_kind":"pith_short_16","alias_value":"CMTDPJFFBERUDUHB","created_at":"2026-07-05T12:11:29.227226+00:00"},{"alias_kind":"pith_short_8","alias_value":"CMTDPJFF","created_at":"2026-07-05T12:11:29.227226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25693","citing_title":"From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07894","citing_title":"TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I","json":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I.json","graph_json":"https://pith.science/api/pith-number/CMTDPJFFBERUDUHBYF73MTRY4I/graph.json","events_json":"https://pith.science/api/pith-number/CMTDPJFFBERUDUHBYF73MTRY4I/events.json","paper":"https://pith.science/paper/CMTDPJFF"},"agent_actions":{"view_html":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I","download_json":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I.json","view_paper":"https://pith.science/paper/CMTDPJFF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.10852&json=true","fetch_graph":"https://pith.science/api/pith-number/CMTDPJFFBERUDUHBYF73MTRY4I/graph.json","fetch_events":"https://pith.science/api/pith-number/CMTDPJFFBERUDUHBYF73MTRY4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I/action/storage_attestation","attest_author":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I/action/author_attestation","sign_citation":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I/action/citation_signature","submit_replication":"https://pith.science/pith/CMTDPJFFBERUDUHBYF73MTRY4I/action/replication_record"}},"created_at":"2026-07-05T12:11:29.227226+00:00","updated_at":"2026-07-05T12:11:29.227226+00:00"}