EvolveTool-Bench: Evaluating the Quality of LLM-Generated Tool Libraries as Software Artifacts
Summary: arXiv:2604.00392v1 Announce Type: cross
In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) are making strides in generating their own tools during runtime. These tools range from Python functions to API clients, significantly enhancing the capabilities of LLMs. However, existing benchmarks predominantly focus on the successful completion of downstream tasks, neglecting crucial aspects of software quality. This approach is akin to evaluating a software engineer solely based on whether their code executes, disregarding vital factors like redundancy, regression, and safety. To address this gap, the introduction of EvolveTool-Bench marks a significant advancement in the evaluation of LLM-generated tool libraries within software engineering workflows.
Introducing EvolveTool-Bench
EvolveTool-Bench serves as a diagnostic benchmark designed specifically for assessing the quality of tool libraries generated by LLMs. The benchmark is structured around three distinct domains that necessitate actual tool execution:
- Proprietary data formats
- API orchestration
- Numerical computation
Within these domains, EvolveTool-Bench defines a variety of library-level software quality metrics that are essential for a comprehensive evaluation:
- Reuse: Measures the ability of tools to be utilized across different tasks without significant modification.
- Redundancy: Assesses the presence of duplicate functionalities within the library.
- Composition Success: Evaluates how well tools work together within a library environment.
- Regression Stability: Monitors the consistency of tool performance over time.
- Safety: Ensures that tools operate without introducing security vulnerabilities or operational risks.
Additionally, EvolveTool-Bench introduces a per-tool Tool Quality Score that assesses individual tools based on four critical dimensions:
- Correctness: The accuracy of the tool in performing its intended function.
- Robustness: The tool’s ability to handle unexpected inputs or conditions.
- Generality: The extent to which a tool can be applied to various tasks.
- Code Quality: The overall quality of the code generated by the tool.
Key Findings and Implications
In a groundbreaking head-to-head comparison of code-level and strategy-level tool evolution, the study analyzed systems using EvolveTool-Bench. This comparison involved three methodologies: ARISE, EvoSkill, and one-shot baselines across 99 tasks with two models. The results revealed that, despite similar task completion rates (ranging from 63% to 68%), the health of the tool libraries varied significantly, with differences of up to 18% in library health metrics. This discrepancy underscores the hidden risks associated with software quality that are not apparent through task-only evaluations.
The findings from EvolveTool-Bench emphasize the necessity of treating evolving tool libraries as first-class software artifacts. This perspective calls for a paradigm shift in how LLM-generated tools are evaluated and governed, moving beyond simplistic task completion metrics to a more holistic understanding of software quality. As LLMs continue to evolve, frameworks like EvolveTool-Bench will be crucial in ensuring the reliability and safety of AI-generated tools in real-world applications.