MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert
  Aggregation
Authors: name: Shoubin Yu, name: Yue Zhang, name: Ziyang Wang, name: Jaehong Yoon, name: Mohit Bansal

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.