研究有何新意？
您可能已经熟悉了LinkedIn上的研究焦点通讯(以新标签打开). 你可以在我们的社交频道、YouTube 和主要播客平台上
在Microsoft Research Focus这一版中，主持人Sowbhagya Suresh分享了一些来自Microsoft的最新消息和尖端研究，从了解编码agents所面临的挑战，到揭示人们如何在工作中使用AI，以及改进延伸范围的天气预报。
研究者今天正在探索各种想法、工具和发现
章节
00: 48：自我改进代理框架(在新标签中打开)
01: 35：理解编码 agent 成功与失败(在新标签中打开)
02:29：用活动日志发展agent技能(在新标签中打开)
03: 22：超越AI使用的相似性(在新标签中打开)
04:15：帮助教师增加教学时间(在新标签中打开)
04:52：改进延程天气预报(新制表开启).
05:42: BioEmu2 现在可公开预览(打开新标签)
06: 32：最后想法(在新标签中打开)
订阅 Microsoft 研究播客：
语录
[音乐]
你好，我是索布哈吉亚·苏雷什。 欢迎来到Microsoft研究焦点。
你可能读过我们的通讯。 现在，你可以从Microsoft的研究圈里得到一些亮点，在YouTube上，以及主要的播客平台上。 无论你阅读，观看，还是听它，Research Focus是你的来源在Microsoft的研究新闻。
接下来是：每天AI使用揭示来描述我们的工作方式；新的AI模型如何帮助科学家更好地理解蛋白质；提前几周改善天气预报的新方法；以及更多。 因此
自我改进的代理框架
越来越多的AI agents正在处理复杂的任务。
[MUSIC FDES] (西班牙语).
需要多个步骤、决定和与外部工具互动的任务。 但是我们怎么能指望AI agents来做这个工作呢？如果他们有能力在事情出错时进行调整，可能需要大量的资源，而得不到更好的结果的保证？
一个新的自我改进框架叫做ForkPilot，当它值得重新审视先前的决定时，就应该根据新的结果寻找不同的方法和调整其战略。
在经过6个基准测试和领先AI模型的测试后，ForkPilot在将AI处理需求降低至59%的同时，对最佳现有方法进行了测量。 这些结果表明，AI agents能够更加可靠和高效地解决复杂的问题，使其更切合实际，更具成本效益地进行大规模部署。
[戏剧音乐]
理解编码 agent 成功与失败
编码agents通过推理，编辑和运行代码执行用户查询。 理解这些agents为何失败，如果我们想建立更有能力和可靠的编码agents，那就是至关重要的。 不幸的是，现有的基准测试往往难以确定哪些因素促成了成败。
CABRA是一个从零开始产生编码挑战的框架。 它让研究者以控制的方式增加复杂性，并确定了agents最难掌握的技能。
研究人员发现，agents经常通过使用工具来克服编码问题。 然而agents在需要了解陌生的密码库时仍然挣扎着。 调查结果表明，理解而不是修改现有守则仍然是发展更有效的编码agents的最大障碍之一。
[戏剧音乐]
以活动日志发展 agent 技能
计算机用途agents是AI系统，旨在完成需要点击和打字的任务。 他们可以填写表格、处理请求和浏览网站。 为了有效地做到这一点，他们需要学习人们用来完成工作的程序。 用户活动日志可以提供一种获取这种知识的方法。 但活动日志一般不记录每个动作背后的意图
新框架TeleTune从这些日志中学习了可重复使用的技能和工作流程。 它还推断了与行动顺序有关的任务目标。
在跨软件和浏览器任务的评价中，TeleTune的表现超过了现有的方法。 通过学习现有活动，TeleTune提供了一个可扩展的方式，帮助AI agents适应不熟悉的软件，并进行复杂，多步骤的工作流程。
[戏剧音乐]
超越AI使用中的相似性( 在新标签中打开)
不同行业的工人可能似乎以惊人的类似方式使用AI，但仔细看揭示是一个不同的故事。
Microsoft研究者分析了Copilot聊天活动在广泛的客户类别中的总合，发现编辑文件主导所有分类活动，占20%以上。
但是，当研究人员搁置20个最常见的活动时，客户类别之间的差异就明显增加。 AI最常用的用途可能是隐藏着各组织如何将AI用于更专业工作的重要差异。
对商界领袖来说，找到合适的机会意味着超越使用统计来了解雇员的实际工作、工作方式和结果最为重要。
[戏剧音乐]
帮助教师增加教学时间
新研究显示如何改变全球南方教室的教师工作流程。
印度低资源学校的研究发现，该工具减少了教师在双语课程规划和官僚主义方面花费的时间。 这使得他们有更多的时间进行更多的参与课堂活动。
超过1 000名教师参加了培训，许多人报告说，压力水平降低，对AI如何帮助他们产生了积极印象。 这些教师积极与Shiksha Copilot接触，利用他们的专业知识来调整和重新使用适合自己教室的材料。
[戏剧音乐]
改进远距离天气预报
10天天气预报已变得非常可靠。 但接下来的几周呢？
两周至六周窗口仍然是预测模型的一大挑战，这是一个问题。 这时，在耕作、管理野火和准备极端天气方面，作出了广泛的重要决定。
一个名为“概率比亚斯校正”的新机器学习框架正在帮助弥合这一差距。 建设和平委员会从一个气象模型过去的错误中吸取教训，并帮助纠正这些错误。
早期的结果很有希望。
建设和平委员会在一次侧重于改进长程预报的全球竞赛中获得了最高荣誉。 也正在肯尼亚部署
这一发展有助于使天气预测在广泛的应用中更加有用。
[戏剧音乐]
BioEmu2 现在可公开预览( 打开新标签)
BioEmu2，一种用于理解生物分子结构，动力学，和功能的新的基础模型，现已公开预览。
Microsoft研究AI for Science团队开发的BioEmu2基于生物分子模拟器，是一种深度学习模型，旨在帮助科学家更好地理解蛋白质是如何工作的。
这种理解可能对医学产生重大影响。 通过更多地了解蛋白质的功能，科学家可以更接近开发更有效的药物，增强有益的蛋白质活性或防止蛋白质产生伤害。
前往BioEmu2 Microsoft研究GitHub寄存器(在新标签中打开)探索模型并学习更多。
[音乐]
这就是你的研究焦点。
从能力较强的AI agents到更好的天气预报，这些只是Microsoft正在探索的一些发展研究者。
想多学多学，保持联系吗？探索最新研究，发现背后的人
我是Sowbhagya Suresh。 非常感谢你调音我们将在下一个 Microsoft 研究焦点中见到你。
[MUSIC FDES] (西班牙语).
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
What’s new in research?
You may already be familiar with the Research Focus newsletter (opens in new tab) on LinkedIn. Now, you can catch up on highlights from across the research community at Microsoft on our social channels, YouTube, and major podcast platforms, too.
In this edition of Microsoft Research Focus, host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting.
Catch up on the ideas, tools, and discoveries researchers are exploring today and get a quick look at what’s coming next.
Chapters
00:48: A self-improving agentic framework (opens in new tab)
01:35: Understanding coding agent success and failure (opens in new tab)
02:29: Evolving agent skills with activity logs (opens in new tab)
03:22: Looking beyond similarities in AI use (opens in new tab)
04:15: Helping teachers make more time for teaching (opens in new tab)
04:52: Improving extended-range weather forecasting (opens in new tab)
05:42: BioEmu2 now available in public preview (opens in new tab)
06:32: Final thoughts (opens in new tab)
Subscribe to the Microsoft Research Podcast:
Transcript
[MUSIC]
SOWBHAGYA SURESH: Hi, I’m Sowbhagya Suresh. Welcome to Microsoft Research Focus.
You’ve probably read our newsletter. And now you can get highlights from across the research community at Microsoft here, on YouTube, and major podcast platforms. Whether you read, watch, or listen to it, Research Focus is your source for research news at Microsoft.
Coming up in this edition: what everyday AI use reveals about the way we work; how a new AI model could help scientists better understand proteins; a new approach to improving weather forecasts weeks in advance; and more. So here’s what’s new.
A self-improving agentic framework
More and more, AI agents are handling tasks that are complex.
[MUSIC FADES]
Tasks that require multiple steps, decisions, and interactions with external tools. But how can we count on AI agents to do the work if their ability to adjust when things go wrong may require a lot of resources with no guarantees of a better result?
A new self-improving framework called ForkPilot learns when it’s worth revisiting earlier decisions to find a different approach and adapts its strategy based on new outcomes.
Tested across six benchmarks and leading AI models, ForkPilot measures up to the best existing methods while reducing AI processing demands by up to 59%. These results suggest that AI agents can solve complex problems more reliably and efficiently, making them more practical and cost-effective to deploy at scale.
[TRANSITION MUSIC]
Understanding coding agent success and failure
Coding agents execute user queries by reasoning about, editing, and running code. Understanding why these agents fail is critical if we want to build more capable and reliable coding agents. Unfortunately, existing benchmarks often make it difficult to identify which factors contribute to success or failure.
CABRA is a framework for generating coding challenges from scratch. It lets researchers increase complexity in a controlled way and pinpoints the skills that are most difficult for agents.
Across thousands of generated challenges, the researchers found that agents often overcame coding problems by using tools. Yet the agents still struggled when they needed to understand unfamiliar codebases. The findings suggest that understanding existing code, rather than modifying it, remains one of the biggest obstacles to developing more effective coding agents.
[TRANSITION MUSIC]
Evolving agent skills with activity logs
Computer-use agents are AI systems that are designed to complete tasks that require clicking and typing. They can fill in forms, process requests, and navigate websites. To do this effectively, they need to learn the procedures that people use to get work done. User activity logs can offer a way to capture this knowledge. But activity logs typically don’t record the intent behind each action and often combine tasks.
The new framework TeleTune learns reusable skills and workflows from these logs. It also infers the task goals associated with the sequences of actions.
In evaluations across software and browser tasks, TeleTune outperformed existing approaches. By learning from existing activity, TeleTune offers a scalable way to help AI agents adapt to unfamiliar software and carry out complex, multi-step workflows
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Looking beyond similarities in AI use (opens in new tab)
Workers across industries may appear to be using AI in surprisingly similar ways, but a closer look reveals a different story.
Microsoft researchers analyzed aggregated Copilot Chat activity across a broad range of customer categories and found that editing documents led all classified activities, accounting for more than 20 percent.
But when researchers set aside the 20 most common activities, differences between customer categories became substantially more pronounced. The most common uses of AI may be hiding important differences in how organizations use it for more specialized work.
For business leaders, the takeaway is clear: don’t assume you have the same AI needs as others in your industry. Finding the right opportunities means looking beyond usage statistics to understand what employees actually do, how they work, and what outcomes matter most.
[TRANSITION MUSIC]
Helping teachers make more time for teaching
A new study shows how Shiksha Copilot—a human-in-the-loop AI tool—could transform teacher workflows in Global South classrooms.
The study in low-resource schools in India found the tool reduced the time teachers spent on bilingual lesson planning and bureaucracy. That gave them more time to craft more engaging classroom activities.
More than a thousand teachers participated, and many reported reduced stress levels and a positive impression of how AI can help them. These teachers actively engaged with Shiksha Copilot, using their professional expertise to adapt and repurpose materials to suit their own classrooms.
[TRANSITION MUSIC]
Improving extended-range weather forecasting
Ten-day weather forecasts have become remarkably dependable. But what about the weeks that follow?
The two-to-six-week window remains a major challenge for forecasting models, which is a problem. That’s when a wide range of critical decisions get made related to farming, managing wildfires, and preparing for extreme weather.
A new machine-learning framework called Probabilistic Bias Correction, or PBC, is helping close that gap. PBC learns from a weather model’s past mistakes and helps correct them.
And the early results are promising.
PBC took top honors in a global competition focused on improving extended-range forecasting. It’s also being deployed in Kenya, where it’s helping improve critical monsoon forecasts for farmers.
This development could help make weather predictions more useful across a broad range of applications.
[TRANSITION MUSIC]
BioEmu2 now available in public preview (opens in new tab)
BioEmu2, a new foundation model for understanding biomolecular structure, dynamics, and function, is now available in public preview.
Developed by the Microsoft Research AI for Science team, BioEmu2 builds on Biomolecular Emulator, a deep-learning model designed to help scientists better understand how proteins work.
That understanding could have important implications for medicine. By learning more about how proteins function, scientists could move closer to developing more effective medications that enhance beneficial protein activity or prevent proteins from causing harm.
Go to the BioEmu2 Microsoft Research GitHub repository (opens in new tab) to explore the model and learn more.
[MUSIC]
And that’s your Research Focus.
From more capable AI agents to better weather forecasting, these are just a few of the developments researchers are exploring at Microsoft.
Want to learn more and stay connected? Explore the latest research, discover the people behind it, and keep tabs on the ideas shaping what’s next by following Microsoft Research on major social platforms.
I’m Sowbhagya Suresh. Thanks so much for tuning in. We’ll see you in the next Microsoft Research Focus.
[MUSIC FADES]
Auto-fetched and translated by AI Atlas. All rights belong to the original author.