AI RESEARCH
LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
arXiv CS.CL
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ArXi:2605.08083v1 Announce Type: new Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS strategies are largely hand-crafted: researchers manually design reasoning patterns and tune heuristics by intuition, leaving much of the computation-allocation space unexplored. We propose an environment-driven framework, AutoTTS, that changes what researchers design: from individual TTS heuristics to environments where TTS strategies can be discovered automatically.