Crucible: Quantifying the Potential of Control Algorithms through LLM Agents

2025-10-21

AI Summary

Existing control algorithm research predominantly evaluates performance under default configurations, neglecting the critical yet underexplored dimension of “tunability potential”—i.e., the extent to which expert-driven parameter tuning can improve real-world deployment performance. Method: This paper formally defines tunability potential and introduces an LLM-driven, multi-level expert simulation framework. Leveraging LLM agents to emulate domain experts’ parameter adjustment strategies, the framework quantifies the practical tunability space of control algorithms across diverse deployment scenarios. It integrates control theory, automated hyperparameter optimization, and empirical closed-loop validation, enabling cross-task and cross-system transferable assessment. Contribution/Results: Experimental evaluation demonstrates that the framework reliably identifies algorithm-specific tunability ceilings and guides effective parameter optimization, yielding significant improvements in closed-loop control performance—thereby bridging the gap between theoretical design and operational efficacy.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of Tuning Potential. To bridge this gap, we introduce Crucible, an agent that employs an LLM-driven, multi-level expert simulation to turn algorithms and defines a formalized metric to quantitatively evaluate their Tuning Potential. We demonstrate Crucible's effectiveness across a wide spectrum of case studies, from classic control tasks to complex computer systems, and validate its findings in a real-world deployment. Our experimental results reveal that Crucible systematically quantifies the tunable space across different algorithms. Furthermore, Crucible provides a new dimension for algorithm analysis and design, which ultimately leads to performance improvements. Our code is available at https://github.com/thu-media/Crucible.

Problem

• Quantifying control algorithm tuning potential through LLM agents
• Evaluating algorithm performance beyond default configurations systematically
• Providing new dimension for algorithm analysis and design improvement

Innovation

• LLM-driven multi-level expert simulation for algorithms
• Formalized metric to quantify tuning potential
• Systematically evaluates tunable space across algorithms