Multimodal Reasoning

Multimodal reasoning studies how models perceive information across modalities, connect that evidence to knowledge, and use it to answer questions, plan actions, or explain decisions. Common approaches include instruction tuning and chain-of-thought training, reinforcement learning with verifiable rewards, tool-using agents, and structured methods such as graphs or symbolic solvers. Benchmarks such as MathVista and MMMU test visual problem solving, while video, document, scientific, and medical tasks probe reasoning across time and specialized evidence. A central challenge is ensuring that correct answers actually depend on relevant multimodal evidence rather than language shortcuts; grounding, cross-modal integration, and reliable generalization remain difficult.