VeriCUA：用智能体探索验证计算机操作代理的任务完成度
VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
做 agent 数据筛选和强化学习的朋友可以看看，VeriCUA 用 GUI 加代码双路取证来验证任务是否真完成，比单纯让 VLM 看截图靠谱。
arXiv 论文提出 VeriCUA，一个跨桌面、移动和网页环境的平台无关验证框架，用于计算机操作代理（CUA）的任务完成判定。传统方法用 VLM 判断轨迹或生成程序检查应用状态，但两者都只依赖环境的部分视图，可能漏掉关键证据。VeriCUA 把验证当作动态取证过程：先根据上下文构建任务评分标准，再对未解决的需求选择 GUI 或代码检查，并按明确的证据规则合并结论。实验显示它与参考标签的一致性高于对比基线，离线困难案例提升最大，在线场景对虚假完成声明的拒绝更强。
VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
Building and scaling computer-using agents (CUAs) across desktop, mobile, and web environments requires reliable task-completion verification for data curation, evaluation, and reinforcement learning. Typical automated approaches prompt vision-language models (VLMs) to judge trajectories or generate programs to inspect application state. However, both rely on partial views of complex digital environments. VLM judges are restricted to supplied observations, while generated programs depend on accessible state. These views can omit decisive evidence or leave completion judgments unsupported. Tailoring separate verifiers to different platforms and access settings adds further engineering cost. We present VeriCUA, a platform-agnostic framework for verification through agentic exploration. It treats verification as dynamic evidence gathering and assessment within a shared procedure for online and offline settings across platforms and observation modalities. After inspecting the available context to construct a task rubric, the verifier selects GUI or code checks for unresolved requirements and combines their findings under explicit evidence rules. Experiments show improved agreement with reference labels over the compared baselines across these settings, with the largest offline gains on hard cases and stronger rejection of false completion claims online. Further analyses connect these gains to complementary evidence and test-time exploration.