CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making

Abstract:Chain-of-Thought (CoT) prompting enables LLMs to perform explicit, step-by-step reasoning, creating opportunities for sophisticated autonomous robots. However, recent research reveals that reasoning models verbalize their actual decision processes only 25-39% of the time, with faithfulness degrading 44% on complex tasks. This paper presents CT-SAFR (Chain-of-Thought Safety and Faithfulness for Robotics), a multi-layered verification framework achieving 94.2% hallucination detection (n = 500, 95% CI: 91.8-95.9%) with sub-500ms latency. Through a warehouse robot case study, this work demonstrates 87% reduction in unsafe reasoning outputs (p < 0.001) and provides recommendations for responsible deployment of reasoning-capable autonomous robots.

7 pages, 4 figures. Accepted version. Published in 2026 IEEE Conference on Artificial Intelligence (CAI), pp. 598-603

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.09692 [cs.RO]

(or arXiv:2609.09692v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.09692 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2026 IEEE Conference on Artificial Intelligence (CAI), pp. 598-603, May 2026

https://doi.org/10.1109/CAI68641.2026.11536646 Focus to learn more DOI(s) linking to related resources

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