Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding

Multimodal intent recognition requires understanding not only what textual, acoustic, and visual signals share, but also how they disagree. Such disagreement is frequently class-informative; for example, lexical positivity accompanied by incongruent vocal or facial behavior may indicate sarcasm or taunting, yet most fusion methods either encourage modality alignment or treat inconsistency as uncertainty to be suppressed. We propose MACH (Modality Agreement- and Conflict-aware prototype Hypergrap

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ethics.ai (4 August 2026), “Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding,” evidence record 16982, https://ethics.ai/record/16982 (originally published by arXiv cs.LG).

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