Multimodal QA

Multimodal question answering studies how models interpret visual, audio, and document evidence alongside language to answer questions, often requiring evidence to be combined across time, pages, or sources. Common approaches include vision-language models, retrieval-augmented generation, tool-using agents, and specialized training for tasks such as chart, medical, and video QA. Benchmarks increasingly test long-context and multi-hop reasoning, evidence localization, temporal understanding, and whether systems know when to abstain. A central challenge is making answers reliably grounded and verifiable while controlling errors, inference cost, and sensitivity to prompts and evaluation methods.