arXiv：cs.AI· Zhentao Xu·· 22 小时前
PID 门控的持续提示词自适应：在噪声反馈下调度指令更新
PID-Gated Continual Prompt Adaptation Under Noisy Feedback
AI 导读
针对 LLM 智能体在非平稳环境中因噪声反馈导致错误累积的问题，研究者提出受 PID 控制启发的轻量门控机制，用于决定反馈何时触发持久的指令更新，并在编辑后回滚性能恶化。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.16129 [cs.LG] |
| (or arXiv:2610.16129v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16129 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhentao Xu [view email]
[v1]
Mon, 12 Oct 2026 03:46:35 UTC (254 KB)
来源：arXiv：cs.AI · arxiv.org