AI 中文总结
SkillLens提出可视化技能卡片（VSCs），结合轨迹转卡片方法与检索增强机制，在两个多模态网页基准上提升了冻结GPT-5.4-mini执行器及Qwen3-VL-2B学生模型的GUI动作预测性能。
AI 中文摘要
使用计算机的智能体能够感知丰富的软件界面，但其决策往往缺乏视觉程序记忆：它们可能识别单个控件，却无法确定哪个熟悉的工作流处于活动状态、接下来哪个控件重要，或哪些证据可确认进展。原始交互轨迹保留了这类信息，但篇幅冗长且存在噪声，难以作为条件使用；而纯文本技能常省略使程序适用的视觉状态。我们引入可视化技能卡片（Visual Skill Cards，VSCs），这是一种状态条件化的记忆表示，将可复用程序与适用性线索、视觉证据及验证信号绑定。SkillLens通过Trace-to-Visual-Skill-Card从异构交互经验构建VSCs；推理时，它检索相关卡片，仅选择性扩展固定视觉语言模型执行器进行 grounded GUI动作预测所需的证据。同一表示还支持CardDistill，该方法将VSC证据作为特权教师上下文，训练无需运行时卡片检索即可行动的学生模型。在Multimodal-Mind2Web和WebLINX-BrowserGym数据集上，SkillLens使冻结的GPT-5.4-mini执行器的步骤成功率（Step SR）分别提升11.6个百分点、整体指标提升2.9个百分点；CardDistill进一步使仅学生模型Qwen3-VL-2B的对应指标分别提升12.0和3.2个百分点。
英文摘要
Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual controls without identifying which familiar workflow is active, which control matters next, or what evidence would confirm progress. Raw interaction traces preserve such information but are long and noisy to condition on, whereas text-only skills often omit the visual state that makes a procedure applicable. We introduce Visual Skill Cards (VSCs), a state-conditioned memory representation that binds reusable procedures with applicability cues, visual evidence, and verification signals. SkillLens constructs VSCs from heterogeneous interaction experience through Trace-to-Visual-Skill-Card and, at inference time, retrieves relevant cards and selectively expands only the evidence needed by a fixed visual-language model executor for grounded GUI action prediction. The same representation also supports CardDistill, which uses VSC evidence as privileged teacher context to train a student that acts without runtime card retrieval. Across Multimodal-Mind2Web and WebLINX-BrowserGym, SkillLens improves the frozen GPT-5.4-mini executor by +11.6 points in Step SR and +2.9 points in Overall, respectively; CardDistill further improves the corresponding student-only Qwen3-VL-2B metrics by +12.0 and +3.2 points.
