PRISM: Perception Reasoning Interleaved for Sequential Decision Making
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, and synthesizes a compact image description. This closed-loop interaction yields a sharp, task-driven understanding of the scene. We evaluate PRISM on...
| Authors | Mohamed Salim Aissi, Clemence Grislain, Clement Romac et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 May 2026 |
| Last updated at source | 12 June 2026 |
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