MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Score
79揭示了视觉工具调用中视觉精度为主瓶颈的新发现，挑战了规划为核心的普遍认知，信息增量大。
框架设计系统，评估完整，失败分析深入，但场景规模和模型覆盖还有提升空间。
对多模态代理研发有直接指导意义，开源代码可复用，但实际应用适配仍需额外工作。
2025年7月最新发布，紧跟多模态代理研究前沿，时效性极强。
苹果研究团队出品，发表在arXiv，代码公开，可信度高。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper builds a large-scale, high-quality evaluation framework supporting multi-turn multi-image tasks, with insightful failure analysis that is highly valuable for the multimodal agent field.
AI commentary
「This paper is worth reading because it shatters the stereotype that 'tool-calling agents' main challenge is planning' with substantial experimental data. For model developers, 51% of failures stem from visual perception rather than decision-making, meaning it's better to first optimize the fine-grained accuracy of vision encoders than to obsess over reasoning chains. For product managers and AI application designers, the scenario generation pipeline in this framework is directly reusable, greatly reducing their own evaluation costs.」
Quick overview
MM-ToolSandBox is a new benchmark and evaluation framework from Apple for visually grounded tool-calling agents. It provides a stateful execution environment with 500+ tools across 16 domains, supporting multi-image, multi-turn tasks with realistic conversational phenomena such as goal revisions and error corrections. An automated pipeline generated 258 human-verified nominal scenarios and 50 UI variants. Evaluating 12 models (from 4B open-weight to frontier proprietary systems), the best model achieves under 50% success rate, with 53% of failures stemming from visual information extraction errors rather than planning. Small models falter in decision-making while large models struggle with perception, indicating different research directions for models at different scales.
- MM-ToolSandBox covers 500+ tools across 16 domains with multi-image, multi-turn, stateful interaction.
- Automated scenario generation with human verification yields 258 nominal scenarios and 50 UI variants.
- The best model achieves under 50% success rate; visual precision is the main bottleneck, with 53% of failures coming from image information extraction errors.
- Small models struggle with decision-making, large models with perception, requiring different improvement strategies for different scales.
Full text
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