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
