MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation

TL;DR

MEXA is a training-free framework that dynamically aggregates specialized expert models for effective multimodal reasoning across diverse domains, improving performance without additional training.

Contribution

Introducing MEXA, a modular, task- and modality-aware aggregation framework that enables flexible multimodal reasoning without extra training overhead.

Findings

Outperforms strong multimodal baselines on various benchmarks.

Effectively handles diverse modalities like video, audio, and medical data.

Provides interpretable reasoning outputs from expert models.

Abstract

Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diversity of input modalities and task complexity. For instance, medical diagnosis requires precise reasoning over structured clinical tables, while financial forecasting depends on interpreting plot-based data to make informed predictions. To tackle this challenge, we introduce MEXA, a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse and distinct domains. MEXA dynamically selects expert models based on the input modality and the task-specific reasoning demands (i.e., skills). Each expert model, specialized in a modality task pair, generates interpretable textual reasoning outputs. MEXA then aggregates and reasons over these outputs using a Large Reasoning Model (LRM) to produce the final answer. This modular design allows flexible and transparent multimodal reasoning across diverse domains without additional training overhead. We extensively evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA. MEXA consistently delivers performance improvements over strong multimodal baselines, highlighting the effectiveness and broad applicability of our expert-driven selection and aggregation in diverse multimodal reasoning tasks.