Adaptive computation for multimodal LLMs drastically reduces compute waste on easy cases while focusing on hard ones.
March 17, 2026
Original Paper
CAMD: Coverage-Aware Multimodal Decoding for Efficient Reasoning of Multimodal Large Language Models
arXiv · 2603.14745
The Takeaway
CAMD provides a theoretical and practical framework for dynamic token allocation based on estimated uncertainty. It allows for more efficient deployment of MLLMs in production by balancing the token budget with reasoning reliability.
From the abstract
Recent advances in Multimodal Large Language Models (MLLMs) have shown impressive reasoning capabilities across vision-language tasks, yet still face the challenge of compute-difficulty mismatch. Through empirical analyses, we identify that existing decoding methods may waste compute on easy cases while underserving hard ones, affecting both model effectiveness and efficiency. To address this issue, we first develop a theoretical framework that links sampling coverage, instance difficulty, and r