From Token to Action: State Machine Reasoning to Mitigate Overthinking
  in Information Retrieval
Authors: name: Dohyeon Lee, name: Yeonseok Jeong, name: Seung-won Hwang

Chain-of-Thought (CoT) prompting enables complex reasoning in large language
models (LLMs), including applications in information retrieval (IR). However,
it often leads to overthinking, where models produce excessively long and
semantically redundant traces with little or no benefit. We identify two key
challenges in IR: redundant trajectories that revisit similar states and
misguided reasoning that diverges from user intent. To address these, we
propose State Machine Reasoning (SMR), a transition-based reasoning framework
composed of discrete actions (Refine, Rerank, Stop) that support early stopping
and fine-grained control. Experiments on the BEIR and BRIGHT benchmarks show
that SMR improves retrieval performance (nDCG@10) by 3.4% while reducing token
usage by 74.4%. It generalizes across LLMs and retrievers without requiring
task-specific tuning, offering a practical alternative to conventional CoT
reasoning. The code and details are available at https://github.com/ldilab/SMR.