{
  "id": 16982,
  "url": "https://arxiv.org/abs/2608.04054v1",
  "title": "Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding",
  "summary": "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",
  "authors": "Mohnish Raj, Suraj Kumar, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-04T10:55:51.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16982",
  "original_url": "https://arxiv.org/abs/2608.04054v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}