Welcome to the Best of CVPR series — your virtual front row to groundbreaking research, insights, and innovations from one of computer vision’s premier conferences. Live from the authors to you.

Date, Time and Location

Jul 08, 2026
9 AM – 11 AM PT
Online. Register for Zoom!

Some Modalities Are More Equal Than Others: Understanding and Improving Multimodal Integration in MLLMs

Multimodal large language models can process vision, audio, and text, but it remains unclear whether they truly integrate these modalities or rely on shortcut cues. In this talk, I will present our recent work, “Some Modalities Are More Equal Than Others,” where we introduce MMA-Bench, a benchmark designed to probe MLLMs under controlled audio–visual conflict, misleading text, and modality-specific queries. Through black-box evaluation and white-box attention analysis, we show that current MLLMs often struggle when modalities disagree, exhibit model-specific modality biases, and can be distracted by irrelevant textual context. We further propose an alignment-aware tuning strategy that trains models to answer based on the queried modality, improving robustness and multimodal grounding. This talk will highlight both the failure modes of current MLLMs and practical directions toward more reliable cross-modal reasoning.