Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text reasoning to enhance both effectiveness and efficiency. However, existing evaluations rely on static difficulty labels and simple metrics, overlooking the model-dependent nature of task difficulties: a task that is challenging for one model may be trivial for another. Consequently, they obscure the distinction between reasoning mode selection and overall task accuracy while neglecting fine-grained reasoning process analyses. In this paper, we propose AdaptMMBench, a comprehensive benchmark for adaptive multimodal reasoning across five domains: natural scene, OCR, GUI, knowledge, and math, encompassing both direct perception and complex reasoning tasks. AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the quality of reasoning mode selection, isolating this meta-cognition ability by dynamically identifying task difficulties based on models’ capability boundaries. Moreover, AdaptMMBench facilitates multi-dimensional reasoning process evaluation across key step coverage, tool effectiveness, and computational efficiency. Our evaluation shows that strong reasoning mode selection is helpful, but final adaptive gains and efficiency also depend on overall model capability. Process metrics further diagnose bottlenecks in reasoning coherence, tool use, and cost for improving adaptive performance and efficiency.

Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theoretically show that generating fine-grained responses poses a reliability challenge, i.e., fine-grained generation is more error-prone than coarse-grained generation. This phenomenon suggests that models should generate the finest description that remains reliable rather than simply produce more specific outputs. To investigate this problem, we develop GranFact, a granularity-aware benchmark consisting of expert-verified multi-object images with coarse-to-fine category annotations. Then, we design a hierarchy-aware evaluation algorithm, which assesses both whether model predictions are visually correct and how specific the correct predictions are. We also propose a reliability-prioritized preference optimization method based on Direct Preference Optimization (DPO), which penalizes unreliable fine-grained claims while rewarding reliable specificity. Experiments on GranFact show that our method improves fine-grained generation while preserving reliability. Code and data are available at https://github.com/WeiWu2025/GranFact.