Flux
Couleur d'accent
Toutes les sources

Microsoft Research

47 articles Flux RSS
IA
Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability
IA

Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim. The research aims to develop robust evaluation methods for long-horizon delegated and […] The post Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability appeared first on Microsoft…

Microsoft Research
mimalloc: A new, high-performance, scalable memory allocator for the modern era
IA

mimalloc: A new, high-performance, scalable memory allocator for the modern era

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy to build and integrate into other projects. It provides bounded worst-case allocation times (up to OS primitives), bounded space overhead, low internal fragmentation, and minimal contention by relying almost exclusively on atomic operations. The post mimalloc: A new, high-performance, scalable…

Microsoft Research
GridSFM: A new, small foundation model for the electric grid
IA

GridSFM: A new, small foundation model for the electric grid

Introducing GridSFM, a small foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings. Learn how GridSFM gives grid operators direct visibility into congestion, stability, and system health. The post GridSFM: A new, small foundation model for the electric grid appeared first on Microsoft Research.

Microsoft Research
Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models
IA

Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. The post Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models appeared first on Microsoft Research.

Microsoft Research
SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests
IA

SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests

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. The post SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests appeared first on Microsoft Research.

Microsoft Research
Building realistic electric transmission grid dataset at scale: a pipeline from open dataset
IA

Building realistic electric transmission grid dataset at scale: a pipeline from open dataset

Microsoft Research is excited to release an open dataset of approximate transmission topology of the U.S. power grid derived from publicly available data. The ability to study transmission-level power grid behavior is essential for modern power systems research. Analyses of congestion, transmission expansion, demand growth, and system resilience all depend on network models with realistic […] The post Building realistic electric transmission grid dataset at scale: a pipeline from open dataset…

Microsoft Research
Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale
IA

Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale

Safe agents don’t guarantee a safe ecosystem of interconnected agents. Microsoft Research examines what breaks when AI agents interact and why network-level risks require new approaches. The post Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale appeared first on Microsoft Research.

Microsoft Research
AutoAdapt: Automated domain adaptation for large language models
IA

AutoAdapt: Automated domain adaptation for large language models

Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requirements is a slow and manual process that is difficult to reproduce. The core challenge is domain adaptation, […] The post AutoAdapt: Automated domain adaptation for large language models appeared first on Microsoft…

Microsoft Research
New Future of Work: AI is driving rapid change, uneven benefits
IA

New Future of Work: AI is driving rapid change, uneven benefits

For the past five years, the New Future of Work report has captured how work is changing. This year, the shift feels especially sharp. Previous editions have focused on technology’s role in increasing productivity by automating tasks, accelerating communication, and expanding access to information, as well as the rise of remote work. Today, generative AI […] The post New Future of Work: AI is driving rapid change, uneven benefits appeared first on Microsoft Research.

Microsoft Research
ADeLe: Predicting and explaining AI performance across tasks
IA

ADeLe: Predicting and explaining AI performance across tasks

AI benchmarks report how large language models (LLMs) perform on specific tasks but provide little insight into their underlying capabilities that drive their performance. They do not explain failures or reliably predict outcomes on new tasks. To address this, Microsoft researchers in collaboration with Princeton University and Universitat Politècnica de València introduce ADeLe (opens in new tab) (AI […] The post ADeLe: Predicting and explaining AI performance across tasks appeared first on…

Microsoft Research
AsgardBench: A benchmark for visually grounded interactive planning
IA

AsgardBench: A benchmark for visually grounded interactive planning

Imagine a robot tasked with cleaning a kitchen. It needs to observe its environment, decide what to do, and adjust when things don’t go as expected, for example, when the mug it was tasked to wash is already clean, or the sink is full of other items. This is the domain of embodied AI: systems […] The post AsgardBench: A benchmark for visually grounded interactive planning appeared first on Microsoft Research.

Microsoft Research
GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation
IA

GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation

Vision-language models (VLMs) use images and text to plan robot actions, but they still struggle to decide what actions to take and where to take them. Most systems split these decisions into two steps: a VLM generates a plan in natural language, and a separate model translates it into executable actions. This approach often breaks […] The post GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation appeared first on Microsoft Research.

Microsoft Research
Esc