PRISM Framework Bridges Perception-Reasoning Gap in Embodied AI
A new framework named PRISM has been developed by researchers to enhance the integration of perception and decision-making in LLM-based embodied agents. This framework tackles the existing gap in perception, reasoning, and decision-making found in standalone Vision-Language Models (VLMs), which frequently overlook essential visual details. PRISM utilizes a dynamic question-answer (DQA) pipeline, allowing the LLM to critique scene descriptions, pose goal-directed inquiries, and create a concise image summary rather than merely accepting VLM outputs. This interactive process results in a focused, task-oriented comprehension of the surroundings. When tested against the ALFWorld and Room-to-Room (R2R) benchmarks, PRISM outperformed leading image-based models significantly. The framework is open-source, and the related paper can be found on arXiv with the identifier 2605.05407.
Key facts
- PRISM is a framework coupling perception (VLM) and decision (LLM) via a dynamic question-answer pipeline.
- It addresses the perception-reasoning-decision gap in standalone VLMs.
- The LLM critiques the VLM's description and probes it with goal-oriented questions.
- PRISM outperforms state-of-the-art image-based models on ALFWorld and R2R benchmarks.
- The framework is fully open-source.
- The paper is published on arXiv with ID 2605.05407.
Entities
Institutions
- arXiv