PID Control

PID: Proportional-Integral-Derivative

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9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

PID-Gated Continual Prompt Adaptation Under Noisy Feedback
Oct 12, 2026Zhentao XuPID Control Prompt Optimization
Independent Researcher, Sunnyvale, CA, USA
ID Balancing: Stable Training of Extremely Sparse MoE via PID-Based Load Control
Sep 30, 2026Peng Jin, Zihan Qiu, Zekun Wang +8Expert Load Balancing Mixture-of-Experts Language Models
Qwen Team, Alibaba Token Hub, Alibaba Group
On Global Regulatability of Robot Manipulators by Classical PID
Sep 1, 2026Cheng Zhao, Jingru Zhu, Lei GuoRobotic Control Feedback Control
State Key Laboratory of Mathematical Sciences, AMSS, Chinese Academy of Sciences, Beijing 100190, China · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
May 18, 2026Le Su, Xing Luo, Zhi JinLLM Quantization Post-Training Quantization
School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong 518107, China · Department of Frontier Research Peng Cheng Laboratory Shenzhen, Guangdong 518055, China
Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
May 10, 2026Akihito Taya, Yuuki Nishiyama, Kaoru SezakiAlternating Direction Method of Multipliers Non-IID Federated Learning
Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan · Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan
Behavior Cloning Under PD Control: A Finite-Horizon Theory of Gain-Dependent Error Amplification
Apr 15, 2026Junghoon SeoPID Control Behavior Cloning
PIT IN Corp., South Korea
Model Predictive Path Integral PID Control for Learning-Based Path Following
Mar 31, 2026Teruki Kato, Koshi Oishi, Seigo ItoNonlinear MPC Residual Dynamics Learning
Toyota Central R&D Labs., Inc., Nagakute 480-1192, Japan
Lyapunov-Based PI-Like Control for Robust Trajectory Tracking of a Four-Wheel Independently Driven and Steered Robot: Design and Experimental Validation
Feb 17, 2026Branimir Ćaran, Vladimir Milić, Marko Švaco +1PID Control Robot Trajectory Tracking
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb HR-10000, Croatia · Croatian Academy of Sciences and Arts, Zagreb 10000, Croatia
Adaptive Activation Steering for Efficient LLM Reasoning via Closed-Loop PID Control
Jun 23, 2025Aryasomayajula Ram BharadwajLanguage Model Steering Efficient Language Model Inference
Independent Researcher

Oct 12, 2026J/K move · Enter open · S save

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

Zhentao Xu

Independent Researcher, Sunnyvale, CA, USA

PID Control Prompt Optimization Continual Learning for LLM Agents Feedback Control

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Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every reported mistake can turn noisy feedback into persistent errors. This raises an often-overlooked scheduling question in prompt adaptation: when should feedback trigger a lasting instruction update, and at what editor-call cost? Taking inspiration from proportional-integral-derivative (PID) control, we introduce a family of lightweight gates that react to the current error, accumulate evidence over time, and roll back deterioration after an edit. We benchmark these gates against both simple baselines and advanced prompt optimizers (TextGrad and GEPA), over a diverse suite of drifting tasks, LLM parameter sizes, feedback-noise levels, and random seeds. PID-gated continual prompt adaptation yields a more favorable Pareto trade-off between task performance (high accuracy, low error) and editor-call budget than the baselines considered, pointing to control-theoretic scheduling as a simple, general tool for robust prompt adaptation under unreliable feedback.