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.

Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2%, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification.

Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) Data Synthesis: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) Training Pipeline: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) Task Evaluation: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area.