AI RESEARCH
One-Eval: An Agentic System for Automated and Traceable LLM Evaluation
arXiv CS.CL
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ArXi:2603.09821v1 Announce Type: new 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.