Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize for user interest. msft.it/6011vPOLF

🚀 Hiring: Research Scientists 🚀 We're hiring Senior and Principal Researchers in Agentic AI at @ms_aifrontiers Lab at @ResearchU Our focus is on developing self-improving agentic systems, agents that learn through interaction with humans and other agents, coordinate and collaborate, and scale into complex real-world environments, covering everything from training and evaluation to deployment. If your expertise includes agentic AI, multi-agent reasoning, continual learning, or synthetic data and evaluation, we want to hear from you! 📝 Apply from below links 📝 apply.careers.microsoft.com/… apply.careers.microsoft.com/…

AI agents shrug off aggressive negotiation tactics. But tell one there's a "Geneva Coffee Convention" capping prices at $2/bean? It folds. Our new research shows that absurd, whimsical strategies — seeded from 2.5K Wikipedia articles — reliably broke even frontier models in simulated negotiations. By grounding generation in diverse external knowledge, we can produce out-of-distribution attacks at scale that standard red-teaming misses. Read more about our findings: microsoft.com/en-us/research… Great work led by @ZacharyHuang12

Hey AutoGen community — we have an important update to share! You know us as the team behind AutoGen, one of the most popular open-source frameworks for building multi-agent systems. What you might not know is that AutoGen came out of MSR AI Frontiers, a boutique lab inside Microsoft Research. AutoGen has since graduated into the Microsoft Agent Framework, where it continues to grow with a broader team. Meanwhile, our lab has kept pushing on the frontier of agents and the models that power them: small models that punch above their weight (Phi-4 Reasoning, Fara-7B), powerful agents that work across the browser and terminal (Magentic-One, Magentic-UI), and new ideas about how models think, reason, and act. We're repurposing this account as the home for MSR AI Frontiers. Same team, still shipping at the bleeding edge of agents, with a lot more to share soon. Follow along!

NEW paper from Microsoft Every agent benchmark has the same hidden problem: how do you know the agent actually succeeded? Microsoft researchers introduce the Universal Verifier, which discusses lessons learned from building best-in-class verifiers for web tasks. It's built on four principles: non-overlapping rubrics, separate process vs. outcome rewards, distinguishing controllable from uncontrollable failures, and divide-and-conquer context management across full screenshot trajectories. It reduces false positive rates to near zero, down from 45% (WebVoyager) and 22% (WebJudge). Without reliable verifiers, both benchmarks and training data are corrupted. One interesting finding is that an auto-research agent reached 70% of expert verifier quality in 5% of the time, but couldn't discover the structural design decisions that drove the biggest gains. Human expertise and automated optimization play complementary roles. Paper: arxiv.org/abs/2604.06240 Learn to build effective AI agents in our academy: academy.dair.ai/