Core Problem & Solution - Current LLM techniques struggle to maintain focus on crucial rules and context in long conversations, leading to hallucinations and inconsistent behavior [1][2][5] - Attentive Reasoning Queries (ARQs) solve this by guiding LLMs with explicit, domain-specific questions encoded as targeted queries inside a JSON schema [3][4] - ARQs reinstate critical instructions and facilitate auditable, verifiable intermediate reasoning steps [4][6] - ARQs outperform Chain-of-Thought (CoT) reasoning and direct response generation, achieving a 90.2% success rate across 87 test scenarios [6][8] Implementation & Application - ARQs are implemented in Parlant, an open-source framework [6] - ARQs are integrated into modules like guideline proposer, tool caller, and message generator [8] - Making reasoning explicit, measurable, and domain-aware helps LLMs reason with intention, especially in high-stakes or multi-turn scenarios [7]

Finally, researchers have open-sourced a new reasoning approach that actually prevents hallucinations in LLMs.It beats popular techniques like Chain-of-Thought and has a SOTA success rate of 90.2%.Here's the core problem with current techniques that this new approach solves:We have enough research to conclude that LLMs often struggle to assess what truly matters in a particular stage of a long, multi-turn conversation.For instance, when you give Agents a 2,000-word system prompt filled with policies, tone r ...