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

• URL: http://arxiv.org/abs/2602.03238v1
• Date: Tue, 03 Feb 2026 08:18:37 GMT
• Title: The Necessity of a Unified Framework for LLM-Based Agent Evaluation
• Authors: Pengyu Zhu, Li Sun, Philip S. Yu, Sen Su,
• Abstract summary: General-purpose agents have seen fundamental advancements.<n> evaluating these agents presents unique challenges that distinguish them from static QA benchmarks.<n>We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation.
• Score: 46.631678638677386
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DREAM: Deep Research Evaluation with Agentic Metrics [21.555357444628044]
  We propose DREAM (Deep Research Evaluation with Agentic Metrics), a framework that makes evaluation itself agentic.<n> DREAM structures assessment through an evaluation protocol combining query-agnostic metrics with adaptive metrics generated by a tool-calling agent.<n>Controlled evaluations demonstrate DREAM is significantly more sensitive to factual and temporal decay than existing benchmarks.
  arXiv  Detail & Related papers  (2026-02-21T19:14:31Z)
• AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition [72.24180896265192]
  We introduce AgentNoiseBench, a framework for evaluating robustness of agentic models under noisy environments.<n>We first conduct an in-depth analysis of biases and uncertainties in real-world scenarios.<n>We then categorize environmental noise into two primary types: user-noise and tool-noise.<n>Building on this analysis, we develop an automated pipeline that injects controllable noise into existing agent-centric benchmarks.
  arXiv  Detail & Related papers  (2026-02-11T20:33:10Z)
• Benchmarking Agents in Insurance Underwriting Environments [0.9728664856449597]
  Existing benchmarks overemphasize open-domains such as code, use narrow accuracy metrics, and lack authentic complexity.<n>We present UNDERWRITE, an expert-first, multi-turn insurance underwriting benchmark designed in close collaboration with domain experts.
  arXiv  Detail & Related papers  (2026-01-31T02:12:11Z)
• Establishing Best Practices for Building Rigorous Agentic Benchmarks [94.69724201080155]
  We show that many agentic benchmarks have issues in task setup or reward design.<n>Such issues can lead to under- or overestimation of agents' performance by up to 100% in relative terms.<n>We introduce the Agentic Benchmark Checklist (ABC), a set of guidelines that we synthesized from our benchmark-building experience.
  arXiv  Detail & Related papers  (2025-07-03T17:35:31Z)
• Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It [1.6261897792391753]
  We conduct a systematic audit of three widely used reasoning benchmarks, SocialIQa, FauxPas-EAI, and ToMi.<n>We uncover pervasive flaws in both benchmark items and evaluation methodology.
  arXiv  Detail & Related papers  (2025-06-30T13:57:28Z)
• EvalAgent: Discovering Implicit Evaluation Criteria from the Web [82.82096383262068]
  We introduce EvalAgent, a framework designed to automatically uncover nuanced and task-specific criteria.<n>EvalAgent mines expert-authored online guidance to propose diverse, long-tail evaluation criteria.<n>Our experiments demonstrate that the grounded criteria produced by EvalAgent are often implicit, yet specific.
  arXiv  Detail & Related papers  (2025-04-21T16:43:50Z)
• Unbiased Evaluation of Large Language Models from a Causal Perspective [19.897724867351315]
  We present a theoretical formulation of evaluation bias, providing valuable insights into designing unbiased evaluation protocols.<n>We propose the Unbiased Evaluator, an evaluation protocol that delivers a more comprehensive, unbiased, and interpretable assessment of LLMs.
  arXiv  Detail & Related papers  (2025-02-10T16:45:18Z)
• More than Marketing? On the Information Value of AI Benchmarks for Practitioners [42.73526862595375]
  In academia, public benchmarks were generally viewed as suitable measures for capturing research progress.<n>In product and policy, benchmarks were often found to be inadequate for informing substantive decisions.<n>We conclude that effective benchmarks should provide meaningful, real-world evaluations, incorporate domain expertise, and maintain transparency in scope and goals.
  arXiv  Detail & Related papers  (2024-12-07T03:35:39Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.