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Microsoft Research

Data Formulator 0.7: AI-powered data analytics for enterprise data Extending Human Intelligence Through AI MagenticLite, MagenticBrain, Fara1.5: An agentic experience optimized for small models Vega: Zero-knowledge proofs for digital identity in the age of AI Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability mimalloc: A new, high-performance, scalable memory allocator for the modern era GridSFM: A new, small foundation model for the electric grid Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models Building realistic electric transmission grid dataset at scale: a pipeline from open dataset Microsoft at NSDI 2026: Advances in large-scale networked systems Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale AutoAdapt: Automated domain adaptation for large language models Can we AI our way to a more sustainable world? New Future of Work: AI is driving rapid change, uneven benefits Ideas: Steering AI toward the work future we want ADeLe: Predicting and explaining AI performance across tasks AsgardBench: A benchmark for visually grounded interactive planning GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation Will machines ever be intelligent? Systematic debugging for AI agents: Introducing the AgentRx framework PlugMem: Transforming raw agent interactions into reusable knowledge Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model Trailer: The Shape of Things to Come
SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests
Tyler Payne, · 2026-05-12 · via Microsoft Research
Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail …

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