title: From Token to Action: State Machine Reasoning to Mitigate Overthinking
    in Information Retrieval
summary: 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.
paper_authors._id: 683b4b86c4b9677f3f7125e5
paper_authors.name: Dohyeon Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:50:07.024Z
paper_authors._id: 6683c3b04905815dcffe7a21
paper_authors.avatarUrl: /avatars/73c50843d99b6fb8b1ee8fe11106c4ce.svg
paper_authors.fullname: Dohyeon Lee
paper_authors.type: user
paper_authors.user: waylight3
paper_authors._id: 683b4b86c4b9677f3f7125e6
paper_authors.name: Yeonseok Jeong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:42:20.758Z
paper_authors._id: 645aedd221ab438e732bff43
paper_authors.avatarUrl: /avatars/31e14fee670fd5ddd296ea0249dbf710.svg
paper_authors.fullname: Yeonseok Jeong
paper_authors.type: user
paper_authors.user: yeonseokjeong
paper_authors._id: 683b4b86c4b9677f3f7125e7
paper_authors.name: Seung-won Hwang
paper_title: From Token to Action: State Machine Reasoning to Mitigate Overthinking
    in Information Retrieval
paper_summary: 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.
paper_ai_summary: State Machine Reasoning (SMR) improves information retrieval performance and reduces token usage in large language models by addressing overthinking through a discrete action framework.
paper_ai_keywords: Chain-of-Thought State Machine Reasoning IR redundant trajectories misguided reasoning early stopping nDCG@10