{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FY3YP2AV24OFZSOTPYE6KAHST2","short_pith_number":"pith:FY3YP2AV","schema_version":"1.0","canonical_sha256":"2e3787e815d71c5cc9d37e09e500f29eac36242ab786a663284418b496d01fa2","source":{"kind":"arxiv","id":"2412.15266","version":1},"attestation_state":"computed","paper":{"title":"On the Structural Memory of LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinyuan Fang, Ruihong Zeng, Siwei Liu, Zaiqiao Meng","submitted_at":"2024-12-17T04:30:00Z","abstract_excerpt":"Memory plays a pivotal role in enabling large language model~(LLM)-based agents to engage in complex and long-term interactions, such as question answering (QA) and dialogue systems. While various memory modules have been proposed for these tasks, the impact of different memory structures across tasks remains insufficiently explored. This paper investigates how memory structures and memory retrieval methods affect the performance of LLM-based agents. Specifically, we evaluate four types of memory structures, including chunks, knowledge triples, atomic facts, and summaries, along with mixed mem"},"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":"2412.15266","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T04:30:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"90df39ccc376e3a927dea500bdb3386f0747ca274b6dc32acfe6e4c3481a8992","abstract_canon_sha256":"061af7cc74c611fab59a295b19c9f74d6fb2d9a563870c781bb2030bf165aee5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:00.770736Z","signature_b64":"ERmps4y6Fg9StEmjdvv0BDyu5n2YtVnJRv2g2M6F/5l0DDMhIJJKR9yco9KIcCXwQVzX4jWKQgB/Qt8zGZj5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e3787e815d71c5cc9d37e09e500f29eac36242ab786a663284418b496d01fa2","last_reissued_at":"2026-07-05T09:52:00.770283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:00.770283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Structural Memory of LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinyuan Fang, Ruihong Zeng, Siwei Liu, Zaiqiao Meng","submitted_at":"2024-12-17T04:30:00Z","abstract_excerpt":"Memory plays a pivotal role in enabling large language model~(LLM)-based agents to engage in complex and long-term interactions, such as question answering (QA) and dialogue systems. While various memory modules have been proposed for these tasks, the impact of different memory structures across tasks remains insufficiently explored. This paper investigates how memory structures and memory retrieval methods affect the performance of LLM-based agents. Specifically, we evaluate four types of memory structures, including chunks, knowledge triples, atomic facts, and summaries, along with mixed mem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15266","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/2412.15266/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":"2412.15266","created_at":"2026-07-05T09:52:00.770350+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15266v1","created_at":"2026-07-05T09:52:00.770350+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15266","created_at":"2026-07-05T09:52:00.770350+00:00"},{"alias_kind":"pith_short_12","alias_value":"FY3YP2AV24OF","created_at":"2026-07-05T09:52:00.770350+00:00"},{"alias_kind":"pith_short_16","alias_value":"FY3YP2AV24OFZSOT","created_at":"2026-07-05T09:52:00.770350+00:00"},{"alias_kind":"pith_short_8","alias_value":"FY3YP2AV","created_at":"2026-07-05T09:52:00.770350+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19135","citing_title":"A Technical Taxonomy of LLM Agent Communication Protocols","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11680","citing_title":"Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2511.02805","citing_title":"MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06330","citing_title":"Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06365","citing_title":"From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08276","citing_title":"ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2","json":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2.json","graph_json":"https://pith.science/api/pith-number/FY3YP2AV24OFZSOTPYE6KAHST2/graph.json","events_json":"https://pith.science/api/pith-number/FY3YP2AV24OFZSOTPYE6KAHST2/events.json","paper":"https://pith.science/paper/FY3YP2AV"},"agent_actions":{"view_html":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2","download_json":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2.json","view_paper":"https://pith.science/paper/FY3YP2AV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15266&json=true","fetch_graph":"https://pith.science/api/pith-number/FY3YP2AV24OFZSOTPYE6KAHST2/graph.json","fetch_events":"https://pith.science/api/pith-number/FY3YP2AV24OFZSOTPYE6KAHST2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2/action/storage_attestation","attest_author":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2/action/author_attestation","sign_citation":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2/action/citation_signature","submit_replication":"https://pith.science/pith/FY3YP2AV24OFZSOTPYE6KAHST2/action/replication_record"}},"created_at":"2026-07-05T09:52:00.770350+00:00","updated_at":"2026-07-05T09:52:00.770350+00:00"}