The field of Multimodal Large Language Models (MLLMs) is rapidly advancing, with a strong focus on enhancing the models' ability to perform complex, integrated reasoning across text and images. Recent developments highlight a shift towards creating benchmarks and frameworks that challenge MLLMs to engage in multi-step, cross-modal reasoning, moving beyond simple visual or textual tasks to more sophisticated, integrated problem-solving scenarios. Innovations in this area include the introduction of new benchmarks that require advanced reasoning capabilities, the development of models that can perform free-form and accurate grounding across multiple images, and the exploration of novel training paradigms that emphasize step-by-step visual reasoning. These advancements underscore the importance of improving multimodal architectures and training methodologies to bridge the gap between human and model reasoning capabilities.

Noteworthy papers include:

• EMMA: Introduces a benchmark targeting organic multimodal reasoning, revealing significant limitations in current MLLMs' handling of complex tasks.
• ReFocus: Presents a framework that enhances structured image understanding through visual editing, significantly improving performance on table and chart tasks.
• Migician: The first multi-image grounding model capable of free-form and accurate grounding across multiple images, outperforming existing MLLMs.
• LlamaV-o1: Proposes a comprehensive framework for step-by-step visual reasoning, introducing a novel metric and a model that outperforms existing open-source models.
• SVE-Math: Addresses the challenge of fine-grained visual understanding in mathematical problem-solving, proposing a model that significantly outperforms others in its class.
• MVoT: Introduces a new reasoning paradigm that enables visual thinking in MLLMs, showing robust improvements in complex spatial reasoning tasks.