The October 10, 2025 paper from the University of Michigan and Google DeepMind concerning the phenomenon of "overthinking" in Large Language Models (LLMs) that utilize chain-of-thought (CoT) reasoning. The authors introduce a systematic analyzer called TRACE to structurally examine an LLM's thought process, decomposing it into sub-thoughts and progression graphs to move beyond superficial, length-based metrics of overthinking. Benchmarking across various tasks reveals that "thinking models" often waste significant computational resources on simple queries without notable accuracy gains, operating five to twenty times slower than non-thinking counterparts. The study identifies two primary overthinking patterns—Explorer (characterized by over-exploration and backtracking) and Late Landing (marked by excessive self-verification)—and proposes a utility-based redefinition of overthinking focused on diminishing marginal returns of subsequent thoughts. Source: https://arxiv.org/pdf/2510.07880