arXiv:2605.05407v2 Announce Type: replace Abstract: Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remains a major challenge. Recent work identifies a perception-reasoning-decision gap in standalone Vision-Language Models (VLMs), which often overlook task-critical information. In this paper, we introduce PRISM, a framework that tightly couples perception (VLM) and decision (LLM) through a dynamic question-answer (DQA) pipeline. Instead of passively accepting the VLM's description, the LLM critiques it, probes the VLM with goal-oriented questions, a
The accelerating capabilities of LLMs are pushing researchers to address their limitations in multimodal interaction and sequential decision-making for embodied agents.
This development represents a significant step towards more capable and autonomous AI agents that can interact effectively with complex real-world environments.
The conventional pipeline for Vision-Language Models (VLMs) is evolving from passive description to active, iterative questioning and critique by an LLM.
- · AI Agent developers
- · Robotics companies
- · VLM researchers
- · Integrated AI platforms
- · Standalone passive VLM approaches
- · Developers reliant on simple VLM outputs
More robust and generalizable embodied AI agents will emerge as perception and reasoning become more tightly integrated.
This framework could lead to rapid improvements in automation across various physical industries, from logistics to manufacturing.
The enhanced agency of AI systems might accelerate discussions and regulations concerning AI autonomy and control in complex environments.
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Read at arXiv cs.AI