{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:76J32YIQWRGFGOKWSOX6HTFP5R","short_pith_number":"pith:76J32YIQ","schema_version":"1.0","canonical_sha256":"ff93bd6110b44c53395693afe3ccafec770bb5a216eb6aebff555b21576cf276","source":{"kind":"arxiv","id":"2607.27274","version":1},"attestation_state":"computed","paper":{"title":"Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiacheng Hao, Sen Song, Xinche Zhang, Youlang Du, Yuhao Sun, Zeyuan Li, Zhen Jiang, Zhiyi Lu, Zhiyuan Ma","submitted_at":"2026-07-29T12:34:47Z","abstract_excerpt":"EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and train instance-level classifiers. This assumes that all instances provide equally reliable diagnostic evidence. Multiple instance learning (MIL) avoids inherited labels by treating each subject as a bag. However, EEG datasets contain far fewer subjects than instances, which can limit the quality of the representations learned by end-to-end MIL. We propose BridgeMIL, a two-stage framework that decouples instance represen"},"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":"2607.27274","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T12:34:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cd203def131a2e6826cac4fbe4d05dacee78e0903147212009fe71282a554977","abstract_canon_sha256":"f2267909a4ab2d4c6d16eaa4be9d80c2f699945066a8c085bf62a381b2ecbe9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff93bd6110b44c53395693afe3ccafec770bb5a216eb6aebff555b21576cf276","last_reissued_at":"2026-07-31T00:10:28.790911Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T00:10:28.790911Z"},"graph_snapshot":{"paper":{"title":"Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiacheng Hao, Sen Song, Xinche Zhang, Youlang Du, Yuhao Sun, Zeyuan Li, Zhen Jiang, Zhiyi Lu, Zhiyuan Ma","submitted_at":"2026-07-29T12:34:47Z","abstract_excerpt":"EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and train instance-level classifiers. This assumes that all instances provide equally reliable diagnostic evidence. Multiple instance learning (MIL) avoids inherited labels by treating each subject as a bag. However, EEG datasets contain far fewer subjects than instances, which can limit the quality of the representations learned by end-to-end MIL. We propose BridgeMIL, a two-stage framework that decouples instance represen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27274","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/2607.27274/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":"2607.27274","created_at":"2026-07-31T00:10:28.793385+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27274v1","created_at":"2026-07-31T00:10:28.793385+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27274","created_at":"2026-07-31T00:10:28.793385+00:00"},{"alias_kind":"pith_short_12","alias_value":"76J32YIQWRGF","created_at":"2026-07-31T00:10:28.793385+00:00"},{"alias_kind":"pith_short_16","alias_value":"76J32YIQWRGFGOKW","created_at":"2026-07-31T00:10:28.793385+00:00"},{"alias_kind":"pith_short_8","alias_value":"76J32YIQ","created_at":"2026-07-31T00:10:28.793385+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/76J32YIQWRGFGOKWSOX6HTFP5R","json":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R.json","graph_json":"https://pith.science/api/pith-number/76J32YIQWRGFGOKWSOX6HTFP5R/graph.json","events_json":"https://pith.science/api/pith-number/76J32YIQWRGFGOKWSOX6HTFP5R/events.json","paper":"https://pith.science/paper/76J32YIQ"},"agent_actions":{"view_html":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R","download_json":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R.json","view_paper":"https://pith.science/paper/76J32YIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27274&json=true","fetch_graph":"https://pith.science/api/pith-number/76J32YIQWRGFGOKWSOX6HTFP5R/graph.json","fetch_events":"https://pith.science/api/pith-number/76J32YIQWRGFGOKWSOX6HTFP5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R/action/storage_attestation","attest_author":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R/action/author_attestation","sign_citation":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R/action/citation_signature","submit_replication":"https://pith.science/pith/76J32YIQWRGFGOKWSOX6HTFP5R/action/replication_record"}},"created_at":"2026-07-31T00:10:28.793385+00:00","updated_at":"2026-07-31T00:10:28.793385+00:00"}