ARQ: A New Structured Reasoning Approach for LLMs

Researchers introduced Attentive Reasoning Queries (ARQs), a structured reasoning approach that prevents LLM hallucinations by guiding models through explicit, domain-specific questions encoded in JSON schemas. Unlike free-form techniques like Chain-of-Thought, ARQs force LLMs to follow controlled reasoning steps, achieving a 90.2% success rate compared to 86.1% for CoT. The approach is implemented in Parlant, an open-source framework for building instruction-following agents, where ARQs are integrated into guideline proposers, tool callers, and message generators to maintain alignment throughout multi-turn conversations.