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[2602.22702] Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics
Summary
The paper introduces 'Knob', a physics-inspired framework that enhances neural network calibration by allowing dynamic adjustments to model behavior, improving interpretability and control during inference.
Why It Matters
This research addresses the limitations of static calibration methods in neural networks by integrating principles from classical control theory. It offers a novel approach that enhances model adaptability, making it particularly relevant for applications requiring real-time adjustments and human oversight.
Key Takeaways
- Knob connects deep learning with classical control theory for better model calibration.
- It allows dynamic adjustments to neural network behavior, enhancing interpretability.
- The framework supports dual-mode inference for static and continuous tasks.
- Operators can tune model stability and sensitivity using familiar physical parameters.
- Experimental results validate the effectiveness of the proposed calibration mechanism.
Computer Science > Artificial Intelligence arXiv:2602.22702 (cs) [Submitted on 26 Feb 2026] Title:Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics Authors:Siyu Jiang, Sanshuai Cui, Hui Zeng View a PDF of the paper titled Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics, by Siyu Jiang and 2 other authors View PDF HTML (experimental) 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 ($\zeta$) and natural frequency ($\omega_n$) -- 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-pre...
