The October 10, 2025 academic paper from Google DeepMind and the University of Michigan investigates "overthinking" in large language models (LLMs), a phenomenon where models engage in excessive, inefficient reasoning for simple queries. The authors introduce a systematic analyzer called TRACE (Thought-process Reconstruction and Automated Clustering Engine) to structurally understand how LLMs reason by decomposing the thought process into discrete sub-thoughts and creating progression graphs. Initial benchmarking confirms that models employing long chain-of-thought (CoT) reasoning are significantly slower on simple tasks without substantial accuracy gains, revealing over-verification and over-exploration as the primary drivers of this inefficiency. Based on their findings, the research proposes a utility-based definition of overthinking which identifies the point of diminishing returns in the thought process, moving beyond simple length-based metrics for better management of LLM inference efficiency. Source: https://arxiv.org/pdf/2510.07880