{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RLRV3557YFBRFFQ6MUQHB365IH","short_pith_number":"pith:RLRV3557","schema_version":"1.0","canonical_sha256":"8ae35df7bfc14312961e652070efdd41d7659e828b3f7e0cf61bd032d7829159","source":{"kind":"arxiv","id":"2603.26738","version":3},"attestation_state":"computed","paper":{"title":"SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Gang Pan, Guifeng Deng, Haiteng Jiang, Jiquan Wang, Junyi Xie, Mengfan Niu, Pan Wang, Sha Zhao, Shuying Rao, Tao Li, Wanjun Guo, Xi'ang Chen","submitted_at":"2026-03-22T09:18:04Z","abstract_excerpt":"While automated sleep staging has achieved expert-level accuracy, its clinical adoption is hindered by a lack of auditable reasoning. We introduce SleepVLM, a rule-grounded vision-language model (VLM) that stages sleep from multi-channel polysomnography (PSG) waveform images and generates clinician-readable rationales based on American Academy of Sleep Medicine (AASM) scoring criteria. Utilizing waveform-perceptual pre-training and rule-grounded supervised fine-tuning, SleepVLM achieved Cohen's kappa of 0.767 on a held-out test set (MASS-SS1) and 0.743 on an external cohort (ZUAMHCS), matching"},"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":"2603.26738","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-03-22T09:18:04Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"fe931d90d7113d8402c82e636ed4aa46357ce86a2f3ab43842128bbccfdc222e","abstract_canon_sha256":"32be6c12059152f413cf4a8bedf0d395697f6896d80ab57b0291242b0c962e02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:12.202572Z","signature_b64":"xNsRQbE2FzMvkNkqLytFJMqqZXku1kBXOHPCBg1HKpBtIfqYGgS4IhbhjXjbRB6cw8SgLRuDZZiKDzGzfFTCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ae35df7bfc14312961e652070efdd41d7659e828b3f7e0cf61bd032d7829159","last_reissued_at":"2026-06-03T01:05:12.202087Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:12.202087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Gang Pan, Guifeng Deng, Haiteng Jiang, Jiquan Wang, Junyi Xie, Mengfan Niu, Pan Wang, Sha Zhao, Shuying Rao, Tao Li, Wanjun Guo, Xi'ang Chen","submitted_at":"2026-03-22T09:18:04Z","abstract_excerpt":"While automated sleep staging has achieved expert-level accuracy, its clinical adoption is hindered by a lack of auditable reasoning. We introduce SleepVLM, a rule-grounded vision-language model (VLM) that stages sleep from multi-channel polysomnography (PSG) waveform images and generates clinician-readable rationales based on American Academy of Sleep Medicine (AASM) scoring criteria. Utilizing waveform-perceptual pre-training and rule-grounded supervised fine-tuning, SleepVLM achieved Cohen's kappa of 0.767 on a held-out test set (MASS-SS1) and 0.743 on an external cohort (ZUAMHCS), matching"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.26738","kind":"arxiv","version":3},"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/2603.26738/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":"2603.26738","created_at":"2026-06-03T01:05:12.202149+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.26738v3","created_at":"2026-06-03T01:05:12.202149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.26738","created_at":"2026-06-03T01:05:12.202149+00:00"},{"alias_kind":"pith_short_12","alias_value":"RLRV3557YFBR","created_at":"2026-06-03T01:05:12.202149+00:00"},{"alias_kind":"pith_short_16","alias_value":"RLRV3557YFBRFFQ6","created_at":"2026-06-03T01:05:12.202149+00:00"},{"alias_kind":"pith_short_8","alias_value":"RLRV3557","created_at":"2026-06-03T01:05:12.202149+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/RLRV3557YFBRFFQ6MUQHB365IH","json":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH.json","graph_json":"https://pith.science/api/pith-number/RLRV3557YFBRFFQ6MUQHB365IH/graph.json","events_json":"https://pith.science/api/pith-number/RLRV3557YFBRFFQ6MUQHB365IH/events.json","paper":"https://pith.science/paper/RLRV3557"},"agent_actions":{"view_html":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH","download_json":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH.json","view_paper":"https://pith.science/paper/RLRV3557","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.26738&json=true","fetch_graph":"https://pith.science/api/pith-number/RLRV3557YFBRFFQ6MUQHB365IH/graph.json","fetch_events":"https://pith.science/api/pith-number/RLRV3557YFBRFFQ6MUQHB365IH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH/action/storage_attestation","attest_author":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH/action/author_attestation","sign_citation":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH/action/citation_signature","submit_replication":"https://pith.science/pith/RLRV3557YFBRFFQ6MUQHB365IH/action/replication_record"}},"created_at":"2026-06-03T01:05:12.202149+00:00","updated_at":"2026-06-03T01:05:12.202149+00:00"}