The Necessity of a Unified Framework for LLM-Based Agent Evaluation

TL;DR

This paper argues for a unified, standardized framework to evaluate Large Language Model-based agents, addressing current issues of inconsistency, lack of reproducibility, and confounding factors in existing benchmarks.

Contribution

It highlights the need for standardization in agent evaluation and proposes a framework to improve fairness, transparency, and reproducibility in assessing LLM-based agents.

Findings

Current benchmarks are confounded by extraneous factors.

Fragmented evaluation frameworks hinder fair comparison.

Standardization can improve reproducibility and fairness.

Abstract

With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents presents unique challenges that distinguish them from static QA benchmarks. We observe that current agent benchmarks are heavily confounded by extraneous factors, including system prompts, toolset configurations, and environmental dynamics. Existing evaluations often rely on fragmented, researcher-specific frameworks where the prompt engineering for reasoning and tool usage varies significantly, making it difficult to attribute performance gains to the model itself. Additionally, the lack of standardized environmental data leads to untraceable errors and non-reproducible results. This lack of standardization introduces substantial unfairness and opacity into the field. We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation. To this end, we introduce a proposal aimed at standardizing agent evaluation.