A Unified Framework for the Evaluation of LLM Agentic Capabilities
Pengyu Zhu, Lijun Li, Yaxing Lyu, Qianxin Luo, Jingyi Yang, Yi Liu, Tingfeng Hui, Xinyu Yuan, Li Sun, Sen Su, Jing Shao
Why It Matters
What makes this one worth your time
This framework enables more reliable assessments of LLMs as agents, which is crucial for their deployment in safety-critical applications and for advancing research in AI capabilities.
A comprehensive framework to evaluate LLM agentic capabilities while disentangling environmental effects.
Summary
The paper presents a unified framework for evaluating the agentic capabilities of large language models (LLMs), addressing issues with benchmark interpretation by integrating diverse benchmarks into a standardized format and analyzing the effects of framework and environment on model performance.
Key contributions
- A unified configuration system that standardizes diverse benchmarks for LLM evaluation.
- A methodology that separates framework effects from environment effects in performance assessments.
- Adaptation of 7 widely used benchmarks across various domains for comprehensive analysis.
Notable insights
- The framework's fixed ReAct-style architecture allows for controlled experimentation, isolating intrinsic model capabilities from external influences.
- The introduction of unified metrics for resource consumption provides a new dimension for evaluating LLM performance beyond task success.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential. However, reported benchmark scores often jointly reflect model capability and the implementation choices each benchmark is packaged with, making cross-benchmark results difficult to interpret as clean measurements of the underlying model. In this work, we present a unified framework for the fair evaluation of LLM agentic capabilities. Driven by a unified configuration system, the framework integrates diverse benchmarks into a standardized instruction-tool-environment format, executes agents through a fixed ReAct-style architecture within a controllable sandbox, and provides an optional offline setting that replaces volatile live environments with curated snapshots, so that framework effects and environment effects can be analyzed separately. Building on this, we unify the evaluation methodology under each benchmark's original task-success criteria, while introducing unified metrics for resource consumption and a taxonomy for decision- and execution-level failure attribution. Within this framework, we adapt 7 widely used benchmarks spanning 24 domains across single-agent, multi-agent, and safety-critical scenarios, and conduct a large-scale empirical analysis over 400K rollouts and 5B tokens on 15 models. The results show that scaffold choice and environmental volatility materially shift benchmark outcomes in both directions, allowing our framework to disentangle intrinsic LLM capabilities from framework- and environment-induced artifacts. We further demonstrate its extensibility as a secure testbed for safety-critical domains. Codes and benchmarks at are available at https://github.com/whfeLingYu/A-Unified-Framework-for-the-Evaluation-of-LLM-Agentic-Capabilities, https://huggingface.co/datasets/whfeLingYu/Unified_Agent_Framework.