MMCT-Agent

MMCT-Agent is a multimodal critical-thinking agent framework for complex visual reasoning over images and long-form video. It integrates audio, visual detail, and text, decomposes complex questions into manageable sub-questions, generates adaptive reasoning strategies, and employs a built-in critic to self-verify intermediate answers. Across multiple visual-reasoning benchmarks it outperforms vanilla multimodal LLMs and tool-augmented pipelines that lack structured deliberation.

MMCT-Agent addresses a real limitation in current multimodal models: they tend to process modalities sequentially or independently rather than reasoning over them jointly, and they have no mechanism to catch their own errors. The framework’s decomposition plus adaptive-strategy approach lets the agent handle reasoning that requires relating visual elements across time and grounding them in textual context, while the self-critique mechanism reduces hallucinations. This makes it well suited to high-confidence applications such as document understanding, video analysis, and multimodal search.