评估视觉生成模型不用再烧算力了，这个框架能把时间砍到十分之一，还支持自然语言定制评估，值得搞生成模型的人看看。
Open Evaluation Agent 提出一种模仿人类快速评估策略的智能体框架，将自然语言评估请求分解为子方面，动态生成提示并采样图像或视频，调用评估工具迭代更新计划。实验显示，该框架将评估时间降至传统方法的 10%，同时结果相当。作者还构建了 EA-CoT-10K 语料库，训练出基于 Qwen2.5-3B-Instruct 的 EA-3B 本地规划模型，减少对专有后端的依赖。在 T2I/T2V 基准和开放查询上验证了 API 版智能体，并在四个域内和三个域外 T2V 生成器上测试了 Open-EA。
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
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.