{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C4EBGZ2QJDPVK66HIYD5IVJTUJ","short_pith_number":"pith:C4EBGZ2Q","schema_version":"1.0","canonical_sha256":"170813675048df557bc74607d45533a27627c6803587e2a18d7ea401f0d161b9","source":{"kind":"arxiv","id":"2503.21760","version":2},"attestation_state":"computed","paper":{"title":"MemInsight: Autonomous Memory Augmentation for LLM Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Currey, Jason Cai, Michelle Yuan, Monica Sunkara, Rana Salama, Yassine Benajiba, Yi Zhang","submitted_at":"2025-03-27T17:57:28Z","abstract_excerpt":"Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents a"},"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":"2503.21760","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-27T17:57:28Z","cross_cats_sorted":[],"title_canon_sha256":"4d52fd8f6429910429b44e4d233ef901679cf5940cf528af36c4e355f6234d1e","abstract_canon_sha256":"7b768988ad15d1a8255a569d59ff31e4e9524545166639401e6ed35f8f4d877e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:41.495517Z","signature_b64":"NKa1z/7kzLr+yIYvA6aDxkve8YEVwLYyfnimLfJWGyNMLhMqqyY++spjWnWerSmnL/q61tcb0h5hgnJhCQUuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"170813675048df557bc74607d45533a27627c6803587e2a18d7ea401f0d161b9","last_reissued_at":"2026-07-05T11:46:41.495073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:41.495073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MemInsight: Autonomous Memory Augmentation for LLM Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Currey, Jason Cai, Michelle Yuan, Monica Sunkara, Rana Salama, Yassine Benajiba, Yi Zhang","submitted_at":"2025-03-27T17:57:28Z","abstract_excerpt":"Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.21760","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/2503.21760/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":"2503.21760","created_at":"2026-07-05T11:46:41.495131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.21760v2","created_at":"2026-07-05T11:46:41.495131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.21760","created_at":"2026-07-05T11:46:41.495131+00:00"},{"alias_kind":"pith_short_12","alias_value":"C4EBGZ2QJDPV","created_at":"2026-07-05T11:46:41.495131+00:00"},{"alias_kind":"pith_short_16","alias_value":"C4EBGZ2QJDPVK66H","created_at":"2026-07-05T11:46:41.495131+00:00"},{"alias_kind":"pith_short_8","alias_value":"C4EBGZ2Q","created_at":"2026-07-05T11:46:41.495131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04197","citing_title":"Exploring the Topology and Memory of Consensus: How LLM Agents Agree, Fragment, or Settle When Forming Conventions","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18421","citing_title":"EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2504.15965","citing_title":"From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2601.01885","citing_title":"Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2601.11100","citing_title":"ReCreate: Reasoning and Creating Domain Agents Driven by Experience","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2602.02556","citing_title":"Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2507.21046","citing_title":"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2508.19828","citing_title":"Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00356","citing_title":"MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08256","citing_title":"HyperMem: Hypergraph Memory for Long-Term Conversations","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06716","citing_title":"From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14362","citing_title":"APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ","json":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ.json","graph_json":"https://pith.science/api/pith-number/C4EBGZ2QJDPVK66HIYD5IVJTUJ/graph.json","events_json":"https://pith.science/api/pith-number/C4EBGZ2QJDPVK66HIYD5IVJTUJ/events.json","paper":"https://pith.science/paper/C4EBGZ2Q"},"agent_actions":{"view_html":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ","download_json":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ.json","view_paper":"https://pith.science/paper/C4EBGZ2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.21760&json=true","fetch_graph":"https://pith.science/api/pith-number/C4EBGZ2QJDPVK66HIYD5IVJTUJ/graph.json","fetch_events":"https://pith.science/api/pith-number/C4EBGZ2QJDPVK66HIYD5IVJTUJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ/action/storage_attestation","attest_author":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ/action/author_attestation","sign_citation":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ/action/citation_signature","submit_replication":"https://pith.science/pith/C4EBGZ2QJDPVK66HIYD5IVJTUJ/action/replication_record"}},"created_at":"2026-07-05T11:46:41.495131+00:00","updated_at":"2026-07-05T11:46:41.495131+00:00"}