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Real-time hallucination detection in LLMs is revolutionized by a lightweight adapter that transforms uncertainty into actionable feedback, preventing undesired actions before they occur.
Stop wasting tokens on irrelevant questions: reward models that ask about task relevance and user answerability can slash question count by 41% while matching GPT-5's issue resolution rate.
Forget specialized tools: a standard Unix terminal and clever RL are all you need to beat much larger LLMs at code search.
