这篇论文教你如何让大模型不再自己绕圈圈，用激活引导精准干预推理过程，实测有效提升准确率。
论文提出SOPHIA方法，通过分析推理轨迹中隐状态转换，发现失败轨迹陷入自循环。该方法构建状态对索引的引导向量库，在推理时实时检测自循环并干预。在多个基准测试中，SOPHIA有效提升任务准确率和令牌效率。实验表明，细粒度控制能改善推理质量。
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shaping how a model reasons are prompt based approaches and operate at the input level, offering no fine-grained control over the reasoning process itself. Related work analyzes and discovers latent transition dynamics in the reasoning traces from Large Language Models. Building on this, we statistically characterize these states, and show that failure trajectories get stuck in self-loops, exhausting the token budget without progress toward the final answer. To intervene on these failures, We propose SOPHIA: Steering Of reasoning Processes via Hidden-state Intervention and Activations. We treat each reasoning trace as a sequence of latent states rather than an unstructured texts, and investigate whether inference time interventions can provide fine-grained control over the self-looping reasoning process. We classify every prefix to a latent state, record step level transitions, and use them to construct a bank of steering vectors indexed by state pairs. At inference time, a controller infers the current state and, given a target state, retrieves the corresponding vector and can also detect self-loops online from the transition structure to prevent the model from sinking into a reasoning black hole. Through extensive experiments, our method reliably intervenes on self-loop failures, with steering vectors that generalize to different state pairs. End task accuracy and token efficiency indicate that fine-grained controllability results in better reasoning quality.