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Crucible: Quantifying the Potential of Control Algorithms through LLM Agents

Lianchen Jia, Chaoyang Li, Qian Houde, Tianchi Huang, Jiangchuan Liu, Lifeng Sun

TL;DR

Crucible addresses a practical gap in control algorithm evaluation by defining Tuning Potential as the achievable performance gain from domain-specific tuning, beyond default designs. It introduces a formal framework that combines an LLM-driven, multi-level expert simulation with a normalization-based metric $\mathcal{P}$, built on a Performance Characteristic Vector $V(E_k)$, RMSE distance $\text{dis}$, and environment similarity $\text{sim}$. The approach is validated across classic control tasks (e.g., CartPole) and complex computer-system problems (ABR and scheduling), including real-world deployment, showing that Crucible expands the exploitable optimization space beyond traditional Bayesian tuning and identifies representational capacity and comprehensibility as key determinants of potential. These insights guide targeted algorithm redesign, yielding significant performance improvements and enabling tuning-potent designs to generalize in evolving environments.

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.

Crucible: Quantifying the Potential of Control Algorithms through LLM Agents

TL;DR

Crucible addresses a practical gap in control algorithm evaluation by defining Tuning Potential as the achievable performance gain from domain-specific tuning, beyond default designs. It introduces a formal framework that combines an LLM-driven, multi-level expert simulation with a normalization-based metric , built on a Performance Characteristic Vector , RMSE distance , and environment similarity . The approach is validated across classic control tasks (e.g., CartPole) and complex computer-system problems (ABR and scheduling), including real-world deployment, showing that Crucible expands the exploitable optimization space beyond traditional Bayesian tuning and identifies representational capacity and comprehensibility as key determinants of potential. These insights guide targeted algorithm redesign, yielding significant performance improvements and enabling tuning-potent designs to generalize in evolving environments.

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.
Paper Structure (38 sections, 6 equations, 5 figures, 5 tables, 2 algorithms)

This paper contains 38 sections, 6 equations, 5 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation
  3. LLM-Based Human Behavior Simulation
  4. Control Algorithms in the Real World
  5. From Algorithm Design to Production Deployment
  6. Experimental Validation of the Importance of Potential
  7. Design
  8. Agent Workflow
  9. Formalization of Potential
  10. Performance Characteristic Vector.
  11. Unified Environment Distance and Similarity.
  12. Tuning Potential Definition.
  13. Interaction Between Crucible and Control Algorithms
  14. Evaluation
  15. Experimental Setup
  16. ...and 23 more sections

Figures (5)

  • Figure 1: The Importance of Algorithm Potential in the Real World
  • Figure 2: Crucible System
  • Figure 3: Optimization Space between Crucible and Bayes
  • Figure 4: Improvement in Different Ability
  • Figure 5: CDF After Optimization