Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs
Tianle Chen, Chaitanya Chakka, Arjun Reddy Akula, Xavier Thomas, Deepti Ghadiyaram
2025-12-05
Summary
This paper investigates how well current AI models that process multiple types of information – like images, audio, and text – handle situations where those different pieces of information don't agree with each other.
What's the problem?
Modern AI, specifically Multimodal Large Language Models, are really good at understanding things when all the information lines up. However, this research shows they easily get confused when the information from different sources, like what you see and what you hear, contradicts each other or when given misleading text. Essentially, they aren't very reliable when things aren't straightforward.
What's the solution?
The researchers created a new set of tests, called MMA-Bench, designed to specifically challenge these AI models with conflicting information. They also developed a method called 'modality alignment tuning' which is like teaching the AI to decide which source of information to trust more in different situations, or even to ignore some information altogether. This tuning process helps the AI make more accurate decisions when faced with mismatched or misleading data.
Why it matters?
This work is important because it highlights a major weakness in current AI systems. If we want AI to be truly reliable in the real world, where information is often messy and contradictory, we need to build models that can handle these situations. This research provides tools to understand *why* these models fail and a way to improve their ability to reason across different types of information, making them more trustworthy.
Abstract
Despite remarkable advancements in Multimodal Large Language Models (MLLMs), a fundamental question remains: are MLLMs robust to contradicting modalities? To rigorously study this, we introduce MMA-Bench comprising videos and tasks that probe a model's reliance on specific modalities. Using black-box and white-box interpretability techniques, we provide a critical analysis of the brittleness of both open- and closed-sourced MLLMs. We show that current MLLMs struggle under misaligned audio-visual pairs and simple misleading text, thereby lacking robust multi-modal reasoning. Building on these findings, we propose a modality alignment tuning strategy to teach the model when to prioritize, leverage, or ignore specific modality cues. Through extensive experiments and analysis, we show that our alignment tuning yields demonstrably stronger multimodal grounding. This work provides both interpretability tools and a clear path toward developing MLLMs with intrinsically reliable cross-modal reasoning. Code and dataset will be publicly available.