This research introduces a framework that enables multimodal language models to assess their own confidence when interpreting visual information and making decisions.

• Combines multimodal reasoning with uncertainty quantification to improve reliability
• Enables models to defer decisions when confidence is low, reducing critical errors
• Creates more trustworthy AI systems through confidence calibration
• Addresses key security challenges by providing transparent confidence metrics

For security applications, this approach creates more reliable AI systems that can recognize their own limitations—essential for deploying AI in high-stakes environments where decision confidence is crucial.

Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework