Multimodal Understanding

The field studies how AI can combine evidence from different kinds of input to answer questions, recognize events, retrieve information, and reason about scenes over time. Common approaches include vision-language models and multimodal large language models, trained with contrastive alignment, cross-attention, instruction tuning, and reinforcement learning; some systems add modality-specific experts, retrieval, or tools. Benchmarks test image and video question answering, document and chart understanding, audio-visual reasoning, grounding, and performance across languages and domains. A central challenge is ensuring that answers genuinely depend on relevant evidence from each modality, while handling long inputs efficiently and making capabilities measurable with reliable tests.