[2605.05407] PRISM: Perception Reasoning Interleaved for Sequential Decision Making
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arXiv:2605.05407 (cs)
[Submitted on 6 May 2026]
Title:PRISM: Perception Reasoning Interleaved for Sequential Decision Making
Authors: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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.05407 [cs.AI]
(or arXiv:2605.05407v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.05407
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohamed Salim Aissi [view email] [v1] Wed, 6 May 2026 19:55:50 UTC (7,074 KB)
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