AI RESEARCH
From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models
arXiv CS.AI
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ArXi:2511.10788v3 Announce Type: replace Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view overlooks a fundamental challenge: current LLMs apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks.