arXiv:2606.03812v1 Announce Type: new Abstract: Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue-multi-agent, multi-turn interactions improv
The increasing deployment of LLMs in critical applications necessitates robust methods for safety validation, leading to innovative approaches like agentic dialogue frameworks.
Reliable hazard identification in high-stakes autonomous and industrial systems is paramount for adoption and prevents catastrophic failures, which this research aims to improve.
Current brittle single-turn LLM inference for safety analysis is being enhanced by structured, self-correcting agentic dialogue, leading to more trustworthy AI applications.
- · AI safety engineers
- · Developers of safety-critical systems
- · AI agent framework providers
- · Companies relying on simplistic AI safety checks
- · Traditional, manual safety analysis methods
HAZDIAL and similar frameworks will make AI more reliable in domains like industrial control and autonomous vehicles.
Increased trust in AI will accelerate its integration into complex operational environments previously deemed too risky.
The development of highly sophisticated AI agents for safety could lead to a new paradigm of autonomous system design, where safety is inherently baked into agentic interactions.
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Read at arXiv cs.AI