arXiv:2606.03965v1 Announce Type: new Abstract: 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 ste
The increasing scale and complexity of LLMs necessitate more efficient reasoning methodologies to reduce excessive resource consumption and gain finer control over their outputs.
This development offers a pathway to more controllable and resource-efficient AI reasoning, critical for deploying advanced LLMs in real-world, performance-sensitive applications.
Reasoning processes in LLMs can now be adaptively steered during inference, moving beyond implicit thought control to explicit, agentic management of cognitive steps.
- · AI developers
- · Cloud providers (reduced inference costs)
- · Enterprises adopting LLMs
- · AI agents
- · Inefficient LLM architectures
- · High-latency LLM applications
It directly improves the efficiency and controllability of large language models.
This could accelerate the deployment of complex AI agents by making their reasoning more predictable and less resource-intensive.
More efficient and steerable LLM reasoning could democratize access to advanced AI capabilities by lowering operational costs and improving application reliability.
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Read at arXiv cs.CL