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