Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics

• URL: http://arxiv.org/abs/2602.22702v1
• Date: Thu, 26 Feb 2026 07:25:22 GMT
• Title: Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics
• Authors: Siyu Jiang, Sanshuai Cui, Hui Zeng,
• Abstract summary: Knob is a framework that connects deep learning with classical control theory.<n>Our framework allows operators to tune "stability" and "sensitivity" through familiar physical analogues.
• Score: 7.965536008626047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing neural network calibration methods often treat calibration as a static, post-hoc optimization task. However, this neglects the dynamic and temporal nature of real-world inference. Moreover, existing methods do not provide an intuitive interface enabling human operators to dynamically adjust model behavior under shifting conditions. In this work, we propose Knob, a framework that connects deep learning with classical control theory by mapping neural gating dynamics to a second-order mechanical system. By establishing correspondences between physical parameters -- damping ratio ($ζ$) and natural frequency ($ω_n$) -- and neural gating, we create a tunable "safety valve". The core mechanism employs a logit-level convex fusion, functioning as an input-adaptive temperature scaling. It tends to reduce model confidence particularly when model branches produce conflicting predictions. Furthermore, by imposing second-order dynamics (Knob-ODE), we enable a \textit{dual-mode} inference: standard i.i.d. processing for static tasks, and state-preserving processing for continuous streams. Our framework allows operators to tune "stability" and "sensitivity" through familiar physical analogues. This paper presents an exploratory architectural interface; we focus on demonstrating the concept and validating its control-theoretic properties rather than claiming state-of-the-art calibration performance. Experiments on CIFAR-10-C validate the calibration mechanism and demonstrate that, in Continuous Mode, the gate responses are consistent with standard second-order control signatures (step settling and low-pass attenuation), paving the way for predictable human-in-the-loop tuning.

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