用闭环控制实现 LLM 神经引导，H∞ 控制器提升分布外鲁棒性
Mind Control of LLMs via Closed-Loop Stimulation: Out-of-Distribution Robustness
把神经科学的闭环刺激思路搬到 LLM 引导上，用 H∞ 控制器在分布外场景下稳住模型行为，思路挺新。
论文将 LLM steering 重新表述为反馈控制问题，由模型内部状态决定干预方式，而非固定开环扰动。作者提出基于 H∞ 的引导控制器，限制扰动和线性化失配的敏感度。在多个 steering benchmark 上，该方法超越现有基线，在分布外条件和不同长度复杂度下增益尤为明显。论文还提出部署前指标 robust steerability（S_rob），衡量控制可容忍的动力学失配程度，并发现其随模型规模系统性变化。
Mind Control of LLMs via Closed-Loop Stimulation: Out-of-Distribution Robustness
Brains and large language models need not be considered equivalent systems to reveal an interpretable commonality: both exhibit high-dimensional, nonlinear dynamics whose behavior can only be modeled imperfectly. This creates a shared challenge whenever we seek to intervene in their dynamics. A controller may either rely on a model identified around a particular operating regime and risk failure as the system moves away from that regime, or explicitly account for uncertainty and a changing internal state. Neurostimulation has confronted an analogous problem for decades. The limitations of fixed, open-loop stimulation have motivated closed-loop and state-dependent approaches that condition intervention on the ongoing state of the neural system. Inspired by this principle, we formulate LLM steering as a feedback-control problem in which the network's internal state, rather than a fixed intervention alone, determines how the system is perturbed. We further account for the mismatch between tractable dynamical models and the nonlinear transformer they approximate through robust control. As one principled realization of this framework, we develop an $H_\infty$-based steering controller that limits sensitivity to disturbances and linearization mismatches. Across multiple steering benchmarks, the controller improves performance over existing baselines, with particularly pronounced gains under out-of-distribution conditions and varying length complexities. Beyond controller performance, we introduce robust steerability, $S_{\mathrm{rob}}$, a pre-deployment measure of tolerance to dynamics mismatch; across model-task pairs, it exhibits task-specific structure and varies systematically with model scale. Beyond robust LLM steering, our measure asks not merely whether control is possible, but how much uncertainty it can survive; a principle that may span artificial and biological neural systems.