Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models

2026-08-10

AI Summary

This work addresses the high computational cost, rigidity, and lack of interpretability and user customization in existing evaluation methods for visual generative models. We propose the Evaluation Agent (EA) framework, which emulates human-like rapid judgment from few examples by decomposing natural language evaluation requests into subtasks through multi-turn dynamic interaction. The framework autonomously generates prompts, samples content, invokes tools, and iteratively refines its evaluation plan. Leveraging a locally deployed agent, EA-3B—fine-tuned from Qwen2.5-3B-Instruct with multi-turn reasoning planning, tool calling, and history-conditioned instructions—the method achieves comparable performance to standard T2I/T2V benchmarks while reducing evaluation time to just 10% of conventional approaches. We release the Open-EA framework and the EA-CoT-10K dataset containing 10K reasoning chains, and demonstrate partial cross-family transferability on video generation models.

Abstract

Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive. Existing evaluation methods also rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations. Mimicking how humans quickly form impressions of a model's capabilities from only a few samples, we propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations, offering detailed, user-tailored analyses. Given a natural-language evaluation request, the agent decomposes it into sub-aspects, generates targeted prompts, samples images or videos from the evaluated model, invokes suitable evaluation tools, and iteratively updates its plan from the observed evidence, covering both predefined benchmark dimensions and open-ended user concerns. The framework is thus efficient, promptable, explainable, and scalable across models and tools. Experiments show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results. We further introduce Open Evaluation Agent (Open-EA) by constructing EA-CoT-10K, a corpus of history-conditioned step-level instruction-tuning records derived from multi-round evaluation rollouts, and training EA-3B from Qwen2.5-3B-Instruct as a local planning backbone that preserves the structured reasoning, tool invocation, and summary protocol of the API-based agent while reducing dependence on proprietary backbones. Experiments validate the API-based agent on established T2I/T2V benchmarks and open-ended queries, and evaluate Open-EA on four in-domain and three out-of-domain T2V generator families, showing partial cross-family transfer of the learned policy.

Problem

• visual generative models
• evaluation efficiency
• user-tailored evaluation
• explainable assessment
• computational cost

Innovation

• Evaluation Agent
• Efficient Evaluation
• Promptable Framework
• Open-EA
• Tool-Augmented Reasoning