# Paint What You See：多模态智能体精细视觉工具使用能力基准 EASEL

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-26 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.virxact.com/items/cmtguy7ww0auvroch2kahi2cc
- 原文链接：https://arxiv.org/abs/2608.25417

## AI 摘要

研究提出 EASEL 基准，以参考图引导的绘画重建为核心任务，评估多模态智能体的精细视觉工具使用能力，并配套提供 44 万样本的分阶段课程数据集 EASEL-Data 及模型 EASEL-9B。

## 正文

Evaluation is shifting from static QA toward agentic settings where models act through external tools. We identify a critical yet underexplored capability within this space - dexterous visual tool use: fine-grained, closed-loop parameterized visual action in which models infer tool parameters from visual evidence, and those parameters directly govern the final result. Existing benchmarks cover web navigation, GUI operation, and software engineering, but rarely target this coupling between visual evidence and execution precision. We propose EASEL, a benchmark evaluating a controlled instance of dexterous visual tool use that adopts reference-guided visual reconstruction as its primary proxy task: the agent incrementally paints a canvas to match a reference image. EASEL additionally includes semantic tasks spanning region annotation, handwriting, and path planning. We further provide EASEL-Data, a 440k-sample two-stage curriculum dataset for trajectory supervision, and EASEL-9B to investigate its effect on this capability. Evaluation of 25 models reveals that current multimodal agents systematically struggle on EASEL. Reconstruction similarity bottlenecks at low levels (0.40-0.54), while trajectory diagnostics expose severe closed-loop instability - models typically saturate early or degrade post-peak. Semantic tasks reveal sharp capability boundaries in precision annotation and path planning. EASEL-9B, trained on EASEL-Data, surpasses the base model by a relative 6.3%, ranking third among all evaluated models.