{"paper":{"title":"MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Architecture family predicts robustness to missing modalities better than model size in clinical fusion.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chen Chen, Song Wang, Tianlong Chen, Wugeng Zheng, Ziwen Kan","submitted_at":"2026-05-13T19:32:11Z","abstract_excerpt":"Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modality missing, where an entire channel is absent, and within-modality missing, where a contiguous time segment is lost. No existing benchmark evaluates multiple fusion architectures under both failure modes at controlled severity levels across diverse clinical datasets. We present MuteBench, a benchmark covering 9 datasets from 7 clinical domains, 6 fusion architectures, and 2 missing-data modes over 125,000 samples. Th"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"architecture family is the strongest predictor of robustness, outweighing parameter count","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The nine chosen clinical datasets and six fusion architectures sufficiently represent the range of real-world multimodal physiological signals and sensor-failure patterns so that the observed robustness rankings generalize beyond the tested cases. (Abstract)","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Architecture family predicts robustness to missing modalities better than model size in clinical fusion.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"423ce505b8717e52ff4103d1cebd7ab21cf6c4474081c58f53e9f98b17a5c526"},"source":{"id":"2605.15235","kind":"arxiv","version":1},"verdict":{"id":"f17af8ad-701b-4dd4-a586-b674601037af","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:29:45.622394Z","strongest_claim":"architecture family is the strongest predictor of robustness, outweighing parameter count","one_line_summary":"MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The nine chosen clinical datasets and six fusion architectures sufficiently represent the range of real-world multimodal physiological signals and sensor-failure patterns so that the observed robustness rankings generalize beyond the tested cases. 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