arXiv:2606.09027v1 Announce Type: cross Abstract: Large Language Models enable flexible natural-language planning but remain unreliable in determinism-critical domains due to their probabilistic nature. This limitation is especially problematic in running planning, where violating safety rules can lead to safety risks. We propose SafeRun, a framework for deterministic LLM-based planning via a decoupled architecture. SafeRun separates soft interpretation by an LLM from hard constraint enforcement by a deterministic solver, ensuring strict safety constraints while preserving natural-language fle
The increasing deployment of LLMs in real-world planning scenarios highlights the critical need to address their inherent probabilistic nature, especially in safety-critical applications.
This development addresses a fundamental limitation of LLMs, potentially unlocking their use in deterministic and safety-critical applications, thereby expanding their practical utility significantly.
The ability to enforce strict safety constraints on LLM plans through a decoupled architecture allows for trusted deployment in domains where previous LLM unreliability was a barrier.
- · AI developers
- · Robotics industry
- · Safety-critical domains (e.g., industrial automation, autonomous systems)
- · AI agents
- · Developers relying solely on probabilistic LLM outputs
LLMs can be applied to a wider range of high-stakes planning and control problems requiring deterministic outcomes.
Increased trust in AI systems for critical decision-making leads to broader adoption across various industries, including those previously hesitant.
The development of robust human-AI collaboration frameworks becomes essential as LLMs take on more deterministic roles in operational planning.
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Read at arXiv cs.AI