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arXiv · 2605.05407

PRISM: Perception Reasoning Interleaved for Sequential Decision Making

Mohamed Salim Aissi·Clemence Grislain·Clement Romac·Laure Soulier·Mohamed Chetouani·Olivier Sigaud·Nicolas Thome
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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, and synthesizes a compact image description. This closed-loop interaction yields a sharp, task-driven understanding of the scene. We evaluate PRISM on the ALFWorld and Room-to-Room (R2R) benchmarks. We show that: (1) PRISM significantly outperforms state-of-the-art image-based models, (2) our Interactive goal-oriented perception pipeline yields systematic and substantial gains, and (3) PRISM is fully automatic, eliminating the need for handcrafted questions or answers.

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Mohamed Salim Aissi, Clemence Grislain, Clement Romac, Laure Soulier, Mohamed Chetouani, Olivier Sigaud, Nicolas Thome. 2026-05-06. PRISM: Perception Reasoning Interleaved for Sequential Decision Making. https://arxiv.org/abs/2605.05407

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