Plug-and-Play Clarifier: A Zero-Shot Multimodal Framework for Egocentric Intent Disambiguation

The introduction of the Plug-and-Play Clarifier marks a significant advancement in the field of egocentric AI agents, which have historically struggled with multimodal intent ambiguity due to underspecified language and imperfect visual data. Existing Vision-Language Models often fail to resolve these ambiguities, leading to task failures. The new framework effectively decomposes the problem into three synergistic modules: a text clarifier for linguistic intent, a vision clarifier for real-time feedback, and a cross-modal clarifier for interpreting gestures. Extensive experiments have shown that this innovative approach improves intent clarification performance by approximately 30% and enhances corrective guidance accuracy by over 20%. This progress is crucial for the development of more reliable and effective AI systems capable of understanding and responding to complex human interactions.