Crucible: Quantifying the Potential of Control Algorithms through LLM Agents

TL;DR

Crucible introduces an LLM-based agent that quantifies the tuning potential of control algorithms, providing a formal metric and demonstrating its effectiveness across various control tasks and real-world deployment.

Contribution

This paper presents Crucible, a novel framework that uses LLM-driven simulation to measure and analyze the tuning potential of control algorithms, filling a gap in existing research.

Findings

Crucible effectively quantifies tunable space across algorithms.

It provides a new dimension for algorithm analysis and design.

Demonstrates success in diverse control scenarios and real-world deployment.

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.