{"paper":{"title":"A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"ConfSleepNet resolves conflicts between misaligned modalities to deliver reliable sleep stage classifications using evidential reasoning.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dekui Wang, Jun Feng, Qirong Bu, Wei Zhou, Xingxing Hao, Yunzhi Tian","submitted_at":"2026-05-16T14:47:59Z","abstract_excerpt":"Multi-view learning has been widely applied for sleep stage classification using multi-modal data. However, existing methods typically assume that different modalities are well-aligned, which is often unattainable in real-world scenarios, thereby compromising the reliability of the staging results. In this paper, we propose ConfSleepNet, a conflict-aware evidential framework that dynamically resolves inter-view conflicts. The framework consists of multi-view evidence extraction and conflict-aware aggregation. In the first phase, it learns category-related evidence from different modalities, wh"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Both theoretical analysis and experimental results demonstrate the effectiveness of ConfSleepNet in sleep staging tasks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that hybrid category structures tailored to the inherent characteristics of varying modalities will promote more reasonable evidence learning from each view.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"ConfSleepNet resolves conflicts between misaligned modalities to deliver reliable sleep stage classifications using evidential reasoning.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1088daaca52f33472e82778f48e9170629cc23c33b7ececfc0c6408f66a15770"},"source":{"id":"2605.17021","kind":"arxiv","version":1},"verdict":{"id":"87e2ff38-6d6d-42b7-821c-70bea74906d3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:14:46.682231Z","strongest_claim":"Both theoretical analysis and experimental results demonstrate the effectiveness of ConfSleepNet in sleep staging tasks.","one_line_summary":"ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that hybrid category structures tailored to the inherent characteristics of varying modalities will promote more reasonable evidence learning from each view.","pith_extraction_headline":"ConfSleepNet resolves conflicts between misaligned modalities to deliver reliable sleep stage classifications using evidential reasoning."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17021/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:18.986548Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:21:39.291943Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T19:49:46.059959Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T18:51:58.536922Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.182786Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:24.863763Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d98ac9dc22510099b53bb59dd7f2e96d51adb511be097f76692ffecefd62590e"},"references":{"count":170,"sample":[{"doi":"","year":2025,"title":"Information Fusion , volume=","work_id":"3603861d-59fc-4e0d-b007-8c9c46acf109","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Hu, Shuaicong and Wang, Yanan and Liu, Jian and Yang, Cuiwei , journal=. 2025 , publisher=","work_id":"e3cc9071-288c-44f3-9527-dd5463bffe3b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Shao, Zhimin and Dou, Weibei and Pan, Yu , journal=. 2024 , publisher=","work_id":"deab8c65-9578-4cba-9e5d-e76bc81e0d78","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management , pages=","work_id":"6a11880c-0f1b-4ed5-b194-71231c7f114d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","work_id":"404fbffb-acf2-4dc9-8af5-eb7fb1d052e3","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":170,"snapshot_sha256":"74a958aebed4f434a6715a38fe4fe28c74e4fa7a6168612c0f7233f2306633be","internal_anchors":1},"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"}