Knob: A Physics-Inspired Gating Interface for Interpretable and Controllable Neural Dynamics
arXiv:2602.22702v1 Announce Type: new 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 dynami
arXiv:2602.22702v1 Announce Type: new 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-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.
Executive Summary
This article proposes Knob, a novel framework that integrates classical control theory with deep learning to create an interpretable and controllable neural dynamics interface. By mapping neural gating dynamics to a second-order mechanical system, Knob enables operators to adjust model behavior and tune 'stability' and 'sensitivity' through familiar physical analogues. The framework employs a logit-level convex fusion and second-order dynamics, allowing for dual-mode inference and predictable human-in-the-loop tuning. Experiments on CIFAR-10-C validate the calibration mechanism and demonstrate the control-theoretic properties of Knob. This work advances the field of neural network calibration and control, with potential applications in real-world inference and human-machine interaction.
Key Points
- ▸ Knob integrates classical control theory with deep learning to create an interpretable and controllable neural dynamics interface.
- ▸ Knob employs a logit-level convex fusion and second-order dynamics for dual-mode inference.
- ▸ Experiments on CIFAR-10-C validate the calibration mechanism and demonstrate the control-theoretic properties of Knob.
Merits
Strength
Knob's integration of classical control theory with deep learning provides a novel and effective approach to neural network calibration and control.
Interpretability
Knob's use of physical analogues enables operators to understand and control model behavior in an intuitive and predictable manner.
Flexibility
Knob's dual-mode inference capabilities allow for both standard i.i.d. processing and state-preserving processing for continuous streams.
Demerits
Limitation
The framework assumes a simplified mechanical system, which may not accurately model real-world neural dynamics.
Calibration Performance
The paper focuses on demonstrating the concept rather than achieving state-of-the-art calibration performance.
Scalability
The framework's scalability to large-scale neural networks and complex tasks is not fully addressed.
Expert Commentary
Knob's innovative integration of classical control theory with deep learning has the potential to revolutionize the field of neural network calibration and control. By providing a novel and effective approach to controlling model behavior, Knob's framework addresses the key limitations of existing calibration methods. While the framework assumes a simplified mechanical system and focuses on demonstrating the concept rather than achieving state-of-the-art calibration performance, its scalability to large-scale neural networks and complex tasks is a topic that requires further investigation. Overall, Knob's framework is a significant contribution to the field and has the potential to impact various applications in real-world inference and human-machine interaction.
Recommendations
- ✓ Further research is needed to fully address the scalability of Knob's framework to large-scale neural networks and complex tasks.
- ✓ Investigations into the control-theoretic properties of neural networks and their applications in real-world inference are necessary to fully realize the potential of Knob's framework.
