One-Eval: An Agentic System for Automated and Traceable LLM Evaluation

• URL: http://arxiv.org/abs/2603.09821v1
• Date: Tue, 10 Mar 2026 15:45:51 GMT
• Title: One-Eval: An Agentic System for Automated and Traceable LLM Evaluation
• Abstract summary: One-Eval is an agentic evaluation system that converts natural-language requests into executable evaluations.<n>One-Eval supports more efficient and reproducible evaluation in industrial settings.
• Score: 10.701916838477187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable evaluation is essential for developing and deploying large language models, yet in practice it often requires substantial manual effort: practitioners must identify appropriate benchmarks, reproduce heterogeneous evaluation codebases, configure dataset schema mappings, and interpret aggregated metrics. To address these challenges, we present One-Eval, an agentic evaluation system that converts natural-language evaluation requests into executable, traceable, and customizable evaluation workflows. One-Eval integrates (i) NL2Bench for intent structuring and personalized benchmark planning, (ii) BenchResolve for benchmark resolution, automatic dataset acquisition, and schema normalization to ensure executability, and (iii) Metrics \& Reporting for task-aware metric selection and decision-oriented reporting beyond scalar scores. The system further incorporates human-in-the-loop checkpoints for review, editing, and rollback, while preserving sample evidence trails for debugging and auditability. Experiments show that One-Eval can execute end-to-end evaluations from diverse natural-language requests with minimal user effort, supporting more efficient and reproducible evaluation in industrial settings. Our framework is publicly available at https://github.com/OpenDCAI/One-Eval.

Related papers

• Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models [74.1961755473754]
  Existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations.<n>We propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations.<n>We show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results.
  arXiv  Detail & Related papers  (2026-08-10T14:42:10Z)
• AlphaEval: Evaluating Agents in Production [32.52315519888507]
  We present AlphaEval, a production-grounded benchmark of 94 tasks sourced from seven companies deploying AI agents in their core business.<n>Unlike model-centric benchmarks, AlphaEval evaluates complete agent products as commercial systems, capturing performance variations invisible to model-level evaluation.
  arXiv  Detail & Related papers  (2026-04-14T00:43:20Z)
• DEP: A Decentralized Large Language Model Evaluation Protocol [51.3646001384887]
  Decentralized Evaluation Protocol (DEP) is a decentralized yet unified and standardized evaluation framework.<n>By decoupling users, LLMs, and benchmarks, DEP enables modular, plug-and-play evaluation.<n>We develop DEP Toolkit, a protocol-compatible toolkit that supports features such as breakpoint resume, concurrent requests, and congestion control.
  arXiv  Detail & Related papers  (2026-03-01T16:10:16Z)
• OutboundEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Outbound Evaluation of Xbench's Professional-Aligned Series [36.88936933010042]
  OutboundEval is a comprehensive benchmark for evaluating large language models (LLMs) in intelligent outbound calling scenarios.<n>We design a benchmark spanning six major business domains and 30 representative sub-scenarios, each with scenario-specific process decomposition, weighted scoring, and domain-adaptive metrics.<n>We introduce a dynamic evaluation method that adapts to task variations, integrating automated and human-in-the-loop assessment to measure task execution accuracy, professional knowledge application, adaptability, and user experience quality.
  arXiv  Detail & Related papers  (2025-10-24T08:27:58Z)
• DOCUEVAL: An LLM-based AI Engineering Tool for Building Customisable Document Evaluation Workflows [14.403156040714817]
  We present DOCUEVAL, an AI engineering tool for building customisable evaluators.<n>We show how DOCUEVAL enables both the engineering of evaluators and scalable, reliable document evaluation.
  arXiv  Detail & Related papers  (2025-09-12T08:09:09Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Adapting Vision-Language Models for Evaluating World Models [24.813041196394582]
  We present UNIVERSE, a method for adapting Vision-language Evaluator for Rollouts in Simulated Environments under data and compute constraints.<n>We conduct a large-scale study comparing full, partial, and parameter-efficient finetuning across task formats, context lengths, sampling strategies, and data compositions.<n>The resulting unified evaluator matches the performance of task-specific baselines using a single checkpoint.
  arXiv  Detail & Related papers  (2025-06-22T09:53:28Z)
• SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection [70.23196257213829]
  We propose a scalable and reliable Semantic-level Evaluation framework for Open domain Event detection.<n>Our proposed framework first constructs a scalable evaluation benchmark that currently includes 564 event types covering 7 major domains.<n>We then leverage large language models (LLMs) as automatic evaluation agents to compute a semantic F1-score, incorporating fine-grained definitions of semantically similar labels.
  arXiv  Detail & Related papers  (2025-03-05T09:37:05Z)
• OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain [62.89809156574998]
  We introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain.<n>Our benchmark is characterized by its multi-dimensional evaluation framework.<n>Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets.
  arXiv  Detail & Related papers  (2024-12-17T15:38:42Z)
• CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution [74.41064280094064]
  textbfJudger-1 is the first open-source textbfall-in-one judge LLM. CompassJudger-1 is a general-purpose LLM that demonstrates remarkable versatility. textbfJudgerBench is a new benchmark that encompasses various subjective evaluation tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:56:51Z)
• How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation [1.7812428873698403]
  We propose an entity-centric data labeling methodology that integrates with a unified framework for monitoring summary statistics. These benchmark data sets can then be used for model training and a variety of evaluation tasks.
  arXiv  Detail & Related papers  (2024-04-08T15:53:29Z)

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.