PRISM: Perception Reasoning Interleaved for Sequential Decision Making
📰 ArXiv cs.AI
Learn how PRISM framework improves sequential decision making in embodied agents by interleaving perception and reasoning, and apply it to your own LLM-based projects
Action Steps
- Implement a dynamic question-answer (DQA) pipeline to interleave perception and reasoning in your LLM-based agent
- Use PRISM to tightly couple perception (VLM) and decision (LLM) in your sequential decision making model
- Apply PRISM to complex multimodal settings, such as text-only environments, to improve task-critical information processing
- Evaluate the performance of PRISM in your embodied agent using metrics such as accuracy and efficiency
- Integrate PRISM with other frameworks or models to further improve decision making in sequential tasks
Who Needs to Know This
Researchers and developers working on embodied agents, multimodal settings, and Vision-Language Models (VLMs) can benefit from this framework to improve decision making in complex environments
Key Insight
💡 PRISM framework can bridge the perception-reasoning-decision gap in standalone Vision-Language Models (VLMs) by tightly coupling perception and decision making
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🤖 Improve sequential decision making in embodied agents with PRISM, a framework that interleaves perception and reasoning! #LLMs #VLMs #EmbodiedAgents
Key Takeaways
Learn how PRISM framework improves sequential decision making in embodied agents by interleaving perception and reasoning, and apply it to your own LLM-based projects
Full Article
Title: PRISM: Perception Reasoning Interleaved for Sequential Decision Making
Abstract:
arXiv:2605.05407v1 Announce Type: new 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 accep
Abstract:
arXiv:2605.05407v1 Announce Type: new 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 accep
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