Go to ACL ARR 2026 January homepageOMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning
Keywords: omni-modal, multi-hop reasoning, benchmark
Abstract: Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models suffer from critical limitations, including modality shortcuts and biased reasoning paths. To address these challenges, we propose OMHBench, a novel benchmark designed to rigorously evaluate omni-modal multi-hop reasoning. It consists of 6,144 questions with balanced reasoning paths that are jointly grounded across all three modalities. Extensive evaluation of 13 state-of-the-art models reveals that (1) a large performance gap exists between proprietary and open-source MLLMs and (2) even proprietary models exhibit high sensitivity to reasoning path variations, resulting in asymmetric omni-modal grounding. Notably, models struggle when processing the speech modality, underscoring the need for balanced, multi-hop evaluation of omni-modal intelligence.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond, NLP Applications, Question Answering
Contribution Types: Data resources, Data analysis
Languages Studied: English
Submission Number: 8859
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