{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NH6DJOJRKP4IYJLTEVJVQEB3S7","short_pith_number":"pith:NH6DJOJR","schema_version":"1.0","canonical_sha256":"69fc34b93153f88c2573255358103b97c062fac3483670a77c21964ac7565650","source":{"kind":"arxiv","id":"2607.22566","version":1},"attestation_state":"computed","paper":{"title":"MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Kaize Ding, Zeyu Zhang, Ziqing Wang","submitted_at":"2026-05-31T03:40:07Z","abstract_excerpt":"MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue. Existing medical QA benchmarks largely test short context knowledge or single document grounding, leaving open whether LLMs can use, connect, and abstain over longitudinal patient histories. We build MedLoCoMo from deidentified MIMIC-IV and MIMIC-IV-Note records by constructing admission-level clinical packets, synthesizing grounded doctor-patient conversations, and generating evidence linked QA items over single-admission, cross-admission, and adversarial unanswe"},"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":"2607.22566","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-31T03:40:07Z","cross_cats_sorted":[],"title_canon_sha256":"e923d5a308301ea141daa82319f93bbad551d55d9d3320ebf0d9fbfe1c33b0c6","abstract_canon_sha256":"f2133d30d5d1780469b94b924506db517cf1b346c30c2000dc32a49642487755"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:42.583965Z","signature_b64":"axb57RhGrevDZleescDybCDOjitTrR6+XYoaAeY4C/Fi0zg4bM1tLSYHRA5ygpj7vRNOVTWyLWzQgK2w+w6FDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69fc34b93153f88c2573255358103b97c062fac3483670a77c21964ac7565650","last_reissued_at":"2026-07-28T00:21:42.583158Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:42.583158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Kaize Ding, Zeyu Zhang, Ziqing Wang","submitted_at":"2026-05-31T03:40:07Z","abstract_excerpt":"MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue. Existing medical QA benchmarks largely test short context knowledge or single document grounding, leaving open whether LLMs can use, connect, and abstain over longitudinal patient histories. We build MedLoCoMo from deidentified MIMIC-IV and MIMIC-IV-Note records by constructing admission-level clinical packets, synthesizing grounded doctor-patient conversations, and generating evidence linked QA items over single-admission, cross-admission, and adversarial unanswe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22566","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/2607.22566/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":"2607.22566","created_at":"2026-07-28T00:21:42.583570+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22566v1","created_at":"2026-07-28T00:21:42.583570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22566","created_at":"2026-07-28T00:21:42.583570+00:00"},{"alias_kind":"pith_short_12","alias_value":"NH6DJOJRKP4I","created_at":"2026-07-28T00:21:42.583570+00:00"},{"alias_kind":"pith_short_16","alias_value":"NH6DJOJRKP4IYJLT","created_at":"2026-07-28T00:21:42.583570+00:00"},{"alias_kind":"pith_short_8","alias_value":"NH6DJOJR","created_at":"2026-07-28T00:21:42.583570+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/NH6DJOJRKP4IYJLTEVJVQEB3S7","json":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7.json","graph_json":"https://pith.science/api/pith-number/NH6DJOJRKP4IYJLTEVJVQEB3S7/graph.json","events_json":"https://pith.science/api/pith-number/NH6DJOJRKP4IYJLTEVJVQEB3S7/events.json","paper":"https://pith.science/paper/NH6DJOJR"},"agent_actions":{"view_html":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7","download_json":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7.json","view_paper":"https://pith.science/paper/NH6DJOJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22566&json=true","fetch_graph":"https://pith.science/api/pith-number/NH6DJOJRKP4IYJLTEVJVQEB3S7/graph.json","fetch_events":"https://pith.science/api/pith-number/NH6DJOJRKP4IYJLTEVJVQEB3S7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7/action/storage_attestation","attest_author":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7/action/author_attestation","sign_citation":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7/action/citation_signature","submit_replication":"https://pith.science/pith/NH6DJOJRKP4IYJLTEVJVQEB3S7/action/replication_record"}},"created_at":"2026-07-28T00:21:42.583570+00:00","updated_at":"2026-07-28T00:21:42.583570+00:00"}