A unified framework standardizes LLM agent benchmarks and demonstrates through 400K rollouts that scaffold and environment choices materially alter outcomes, enabling separation of intrinsic capabilities from artifacts.
The Necessity of a Unified Framework for LLM-Based Agent Evaluation
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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.
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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A Unified Framework for the Evaluation of LLM Agentic Capabilities
A unified framework standardizes LLM agent benchmarks and demonstrates through 400K rollouts that scaffold and environment choices materially alter outcomes, enabling separation of intrinsic capabilities from artifacts.