{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4W5TEPHAKCTKYXORSFSZZ6PKMM","short_pith_number":"pith:4W5TEPHA","schema_version":"1.0","canonical_sha256":"e5bb323ce050a6ac5dd191659cf9ea6330b2a5622d79e95a7841806b88ea3c1e","source":{"kind":"arxiv","id":"2606.28349","version":1},"attestation_state":"computed","paper":{"title":"HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Qiang Xu, Yizhou Zhou, Zeju Li, Ziyang Zheng","submitted_at":"2026-06-03T07:15:11Z","abstract_excerpt":"Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-augmented generation reduces this problem to top-$K$ chunk retrieval, but such passive access can discard relevant evidence before reasoning begins, especially when relevance depends on broader context. We propose HMARS, a hierarchical multi-agent memory system that treats long contexts as managed memory rather than a flat retrieval corpus. Sub-agents maintain grounded access to bounded memory regions, mid-agents man"},"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":"2606.28349","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-03T07:15:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bf4a761a03361860ebc5f53935038b2dba68ab98d49aa9be994b9389a617c433","abstract_canon_sha256":"a2f7c1a00bf53750e05ee24ba843b93b37fb24d54b281db93a78ed39b4c8c319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T00:15:11.638611Z","signature_b64":"VRS7t0yGgyTFRV67+iPN5fhr/sso/zkN3pNV0x9BIPxAXgb/kdM/IeFLHKD0MEhqsxgXJoQQkUBsEQxIR5YpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5bb323ce050a6ac5dd191659cf9ea6330b2a5622d79e95a7841806b88ea3c1e","last_reissued_at":"2026-06-30T00:15:11.638199Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T00:15:11.638199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Qiang Xu, Yizhou Zhou, Zeju Li, Ziyang Zheng","submitted_at":"2026-06-03T07:15:11Z","abstract_excerpt":"Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-augmented generation reduces this problem to top-$K$ chunk retrieval, but such passive access can discard relevant evidence before reasoning begins, especially when relevance depends on broader context. We propose HMARS, a hierarchical multi-agent memory system that treats long contexts as managed memory rather than a flat retrieval corpus. Sub-agents maintain grounded access to bounded memory regions, mid-agents man"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28349","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/2606.28349/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":"2606.28349","created_at":"2026-06-30T00:15:11.638259+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.28349v1","created_at":"2026-06-30T00:15:11.638259+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28349","created_at":"2026-06-30T00:15:11.638259+00:00"},{"alias_kind":"pith_short_12","alias_value":"4W5TEPHAKCTK","created_at":"2026-06-30T00:15:11.638259+00:00"},{"alias_kind":"pith_short_16","alias_value":"4W5TEPHAKCTKYXOR","created_at":"2026-06-30T00:15:11.638259+00:00"},{"alias_kind":"pith_short_8","alias_value":"4W5TEPHA","created_at":"2026-06-30T00:15:11.638259+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/4W5TEPHAKCTKYXORSFSZZ6PKMM","json":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM.json","graph_json":"https://pith.science/api/pith-number/4W5TEPHAKCTKYXORSFSZZ6PKMM/graph.json","events_json":"https://pith.science/api/pith-number/4W5TEPHAKCTKYXORSFSZZ6PKMM/events.json","paper":"https://pith.science/paper/4W5TEPHA"},"agent_actions":{"view_html":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM","download_json":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM.json","view_paper":"https://pith.science/paper/4W5TEPHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.28349&json=true","fetch_graph":"https://pith.science/api/pith-number/4W5TEPHAKCTKYXORSFSZZ6PKMM/graph.json","fetch_events":"https://pith.science/api/pith-number/4W5TEPHAKCTKYXORSFSZZ6PKMM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM/action/storage_attestation","attest_author":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM/action/author_attestation","sign_citation":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM/action/citation_signature","submit_replication":"https://pith.science/pith/4W5TEPHAKCTKYXORSFSZZ6PKMM/action/replication_record"}},"created_at":"2026-06-30T00:15:11.638259+00:00","updated_at":"2026-06-30T00:15:11.638259+00:00"}