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

2026-02-26

AI Summary

This work proposes a novel architecture that integrates control theory with deep learning to address the limitations of existing static post-hoc calibration methods, which are ill-suited for dynamic inference and human-in-the-loop regulation. By mapping second-order physical system parameters—damping ratio and natural frequency—onto neural gating mechanisms, the authors introduce a tunable “safety valve” interface based on Knob-ODE. This framework supports both static and continuous-stream inference modes and enables dynamic, interpretable control over model stability and sensitivity through logit-level convex fusion and input-adaptive temperature scaling. Experiments on CIFAR-10-C demonstrate that the gated response exhibits canonical second-order system characteristics, such as stable step responses and low-pass attenuation, thereby validating both the calibration efficacy and the predictability of human-AI co-regulation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• neural calibration
• dynamic inference
• human-in-the-loop control
• temporal modeling
• interpretable interface

Innovation

• physics-inspired gating
• neural calibration
• second-order dynamics
• interpretable control
• human-in-the-loop tuning