AI 模型和平台
OpenAI的寿命评价测试 AI 每日实用任务
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OpenAI于2026年11月2日发布了LifeVal，该评价部分基于一项3,995个响应调查，以跟踪AI模式在一段时间内在实际工作中的表现，包括寻找儿童保育，创造预算和订购杂货。
LifeVal覆盖用户希望AI帮助的地区和个人agents. OpenAI在过去一年中利用评价来监测示范结果和直接改进日常任务。 其衡量目标是实际援助：一个模式是否能够帮助某人作出决定、处理日常责任和采取有意义的行动。 这使得重点超越了对模型知识的学术测试。 其他实际援助的例子包括获得政府服务、准备上学、修改食谱和设计解决计划。
任务选择和时间使用数据
OpenAI开始任务选择，有两个研究涉及美国和其他国家。 《美国时间使用调查》衡量人们花费多少时间从事有偿工作、儿童保育、志愿工作和社会化等活动。 多国时间使用研究带来从100多个随机抽样的国家规模调查到单一标准化格式的100多万日记日记。 它支持分析30个国家在55年期间用于工作和休闲活动的时间。
数据集一起使用，显示日常活动如何因人口群体、日间和季节的不同而变化。 OpenAI补充了一项新的具有人口代表性的调查，调查就每项任务提出了问题，以确定答卷者希望得到个人助理帮助的地方。 由此产生的任务领域包括护理、保健、教育、家庭管理、个人财务、购物和获得政府服务。
五份工作流的得分报告
OpenAI在5个实用工作流程中为目前的模型报告这些分数。 每个实例将用户请求与响应和百分比对齐：
示范培训和ChatGPT的作用
OpenAI 称的型号在去年的训练中变得更快，能力更强，更适合浏览器使用，计算机使用和连接器任务。 这些能力扩大了通过ChatGPT提供的援助范围。 产品方向是一个能进行实际动作的代理个人助理，将ChatGPT的作用扩展到聊天之外。
OpenAI 称它希望评价将有助于推进这个测量目标。
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AI Models & Platforms
OpenAI’s LifeVal Evaluation Tests AI on Practical Everyday Tasks
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OpenAI published LifeVal on November 2, 2026, an evaluation built partly from a 3,995-respondent survey to track AI model performance over time on practical work including finding childcare, creating budgets and ordering groceries.
LifeVal covers areas where users wanted help from AI and personal agents. OpenAI has used the evaluation over the past year to monitor model results and direct improvements on everyday tasks. Its measurement target is practical assistance: whether a model can help someone make decisions, handle routine responsibilities and carry out meaningful actions. That broadens the focus beyond academic tests of model knowledge. Other examples of practical assistance include accessing government services, preparing for school, modifying recipes and designing workout plans.
Task Selection and Time-Use Data
OpenAI began task selection with two studies covering the United States and other countries. The American Time Use Survey measures how much time people spend on activities including paid work, childcare, volunteering and socializing. The Multinational Time Use Study brings more than one million diary days from over 100 randomly sampled national-scale surveys into a single standardized format. It supports analysis of time spent on work and leisure activities across 30 countries over a 55-year span.
Used together, the datasets show how everyday activities vary by demographic group, time of day and season. OpenAI supplemented them with a new demographically representative survey, which asked questions about each task to determine where respondents wanted a personal assistant’s help. The resulting task areas include caregiving, health, education, household administration, personal finances, shopping and accessing government services.
Reported Scores on Five Workflows
OpenAI reports these scores for its current models on five practical workflows. Each example pairs a user request with a response and a percentage:
Model Training and ChatGPT’s Role
OpenAI says its models became faster, more capable and more aligned for browser-use, computer-use and connector tasks during the past year of training. Those capabilities widen the range of assistance available through ChatGPT. The product direction is an agentic personal assistant that can carry out practical actions, extending ChatGPT’s role beyond chat.
OpenAI says it hopes the evaluation will help advance that measurement goal.
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