Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Existing efficient reasoning methods control thinking length by shortening, early-stopping, or compressing traces, leaving how the model thinks implicit. In this paper, we propose Agentic Chain-of-Thought Steering (ACTS), which formulates reasoning steering as a Markov decision process where a controller agent adaptively steers a frozen reasoner during inference. At each step, the controller observes the reasoning trace and remaining thinking budget, then issues a steering action consisting of a reasoning strategy an...
| Authors | Yu Xia, Zhouhang Xie, Xin Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 30 August 2026 |
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