Microsoft open-sources CAVEAT, used to evaluate whether web agents follow users' stated preferences and make optimal choices; the only reported metric is the optimal choice rate. The benchmark covers nine fictional e-commerce and service environments, split into CAVEAT-Standard (nine environments, 312 runs) and CAVEAT-Hard (five scenarios with 2,112 products, 10 runs per model/framework), and comes with a BrowserUse baseline and an improved CAVEAT-Harness, supporting any OpenAI-compatible endpoint and custom harnesses. Under steering techniques, the optimal choice rate drops from 78.6% to 17.3%. A follow-up paper explains that the benchmark uses nine high-fidelity e-commerce environments and eight categories of steering mechanisms drawn from commercial practice to evaluate whether computer-using agents still choose the user-optimal product when the environment and user goals have conflicting incentives; across 52 standard tasks and 18 configurations from five model families, the optimal purchase rate is 78.6% with steering disabled, dropping to 17.3% with all steering enabled, a degradation of 61.3 percentage points; CAVEAT-Hard expands the catalog to 2,112 products and 88 pages of results, where GPT-5.6-Sol-high drops by more than 90 percentage points.
Microsoft Research 提出 MindTopo,一个评测多模态大语言模型拓扑推理能力的基准,覆盖连通性、分离、顺序、包围和绳结五类拓扑关系。基准分两个认知层次:推理任务让模型判断静态场景中的拓扑结构,规划任务让模型在模拟环境中通过一系列动作创建、保持或移除某种关系,环境会拒绝违反物理约束的动作。在多种闭源与开源权重模型上,静态推理表现一致优于交互规划,两者都远低于人类水平;静态错误多源于感知遗漏,规划错误则出现在场景已被理解之后,模型会采取局部可行但未追踪后续后果的动作,或在多轮中丢失任务目标。研究还测试了图像与视频生成能否帮助维持拓扑理解,图像生成仅在单帧可见关系时偶尔有用,视频推演常改变拓扑或违反任务动态。