这个基准测试揭示了当前 AI 代理在理解用户完整数字生活方面的巨大短板，做个人助手或智能体开发的团队值得关注——它直接指出了现有系统为何不够智能，并提供了改进方向。
Claw-Anything 是一个新基准，旨在评估大型语言模型代理作为始终在线个人助手的能力。现有系统仅能访问用户数字世界的狭窄部分，限制了上下文感知推理和有效协助。该基准通过三个维度扩展代理上下文：长期活动历史、相互依赖的后端服务以及跨多设备的 GUI 和 CLI 交互。实验显示，GPT-5.5 仅达到 34.5% 的 pass@1，远低于先前基准，突显了当前代理能力与始终在线个人助手需求之间的差距。同时，研究团队发布了自动化数据生成管道，可生成 2000 个训练环境，并将基础模型性能提升 23.7%。
Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World
Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate over only narrow slices of that world, limiting context-sensitive reasoning and effective assistance. Existing benchmarks similarly provide only partial user state and therefore fail to capture performance in such a broad, always-on setting. To address this gap, we introduce Claw-Anything, a benchmark that expands agent context along three dimensions: long-horizon activity histories, interdependent backend services, and integrated GUI and CLI interaction across multiple devices. To instantiate this setting, we simulate months of user activity through multi-round event injection, producing complex world states and realistic noise, including irrelevant events and conflicting signals. Agents must reason over rich contextual environments while remaining robust to such noise. This expanded scope also enables the evaluation of proactive assistance, requiring agents to anticipate user needs and deliver timely recommendations. Experiments show that GPT-5.5 achieves only 34.5% pass@1, substantially below prior benchmarks, underscoring a gap between current agent capabilities and the demands of always-on personal assistance. Alongside the benchmark, we release an automated data-generation pipeline that yields 2,000 training environments and improves the base model by 23.7%, demonstrating its utility of scalable data infrastructure.