Microsoft Research Focus – Edition #97

Microsoft Research Focus – Edition #97 - Microsoft Research

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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.

Related links:

Research Focus newsletter on LinkedIn (opens in new tab)

ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents

Code Understanding is a Bottleneck for Coding Agents

TeleTune: Evolving Agent Skills from Offline Telemetry

Why Copilot use looks so similar across industries, and what that misses (opens in new tab)

Shiksha Copilot: Teacher-AI Collaboration for Curating and Customizing Lesson Plans in Low-Resource Schools

New AI framework bridges a critical gap in mid-range weather forecasts

BioEmu2 in public preview (opens in new tab)

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)

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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...