Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving
The paper presents a new framework called Reason--Imagine--Act (RIA) for enhancing decision-making in autonomous driving using large language models (LLMs). This closed-loop system integrates an LLM reasoner with a world model to ensure safety during dynamic traffic scenarios. RIA demonstrates improved performance metrics compared to existing methods, achieving significant route completion and low collision rates.
- ▪RIA couples an LLM reasoner with an action-conditioned world model for online safety verification.
- ▪The framework achieved 80.05% route completion and a 0.20% collision rate in testing.
- ▪RIA outperforms training-free baselines on core closed-loop metrics.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24004 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.24004 (cs) [Submitted on 19 May 2026] Title:Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving Authors:Zhengqi Sun, Yiwen Sun, Boxuan Liu, Tailai Chen, Tianxu Guo, Jiabin Liu View a PDF of the paper titled Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving, by Zhengqi Sun and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic traffic.
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