AI RESEARCH
Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models
arXiv CS.AI
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ArXi:2604.08563v1 Announce Type: cross Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for complex problem solving. However, the optimal configuration of sampling temperature and prompting strategy for these systems remains largely underexplored.