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

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

At a glance - Today’s critical services and applications are often highly concurrent, using hundreds of threads. They also operate at large memory scales, frequently hundreds of gigabytes, especially when using large language models. - 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. - mimalloc is available on GitHub (opens in new tab) and has over 12K stars. mimalloc At the RiSE group at Microsoft Research (MSR), we conduct fundamental research into formal methods, programming languages, and software engineering (including emerging agentic systems), with…

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

At a glance - Experimental validation: Using high-throughput screening with MatterSim-v1, we previously identified tetragonal tantalum phosphorus (TaP) as a potential high-performance thermal conductor. Now we have experimentally synthesized it and measured its thermal conductivity (152 W/m/K) to be close to the thermal conductivity of silicon. - Faster simulation: We have accelerated MatterSim-v1 model inference by 3-5x and integrated it with the LAMMPS software package, enabling large-scale simulations across multiple GPUs. - New model release: We are introducing MatterSim-MT, a multi-task foundation model for in silico materials characterization that enables the simulation of complex, multi-property phenomena beyond what potential energy surfaces alone can capture. Materials design underpins a wide range of technological advances, from nanoelectronics to semiconductor design and energy storage. Yet development cycles for novel materials remain slow and costly. Universal machine learning interatomic potentials aim to accelerate the…

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

At a glance - AI agents are moving into social contexts. When agents manage calendars, negotiate purchases, or interact with other agents on a user’s behalf, they need more than task competence—they need social reasoning. - SocialReasoning-Bench evaluates that ability. The benchmark tests whether an agent can negotiate for a user in two realistic settings: Calendar Coordination and Marketplace Negotiation. - The benchmark measures both outcomes and process: it scores agents on outcome optimality (how much value they secure for the user) and due diligence (whether they follow a competent decision-making process). - Current frontier models often leave value on the table. They usually complete the task, but they frequently accept suboptimal meeting times or poor deals instead of advocating effectively for the user. - Prompting helps, but it is not enough. Even with explicit guidance to act in the…

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

At a glance - We construct geographically grounded, electrically coherent power grid models entirely from publicly available data and release a dataset spanning 48 U.S. states and multi-state interconnections. - The models support AC optimal power flow (AC‑OPF) analysis, enabling physics-based study of congestion, capacity, and demand siting without restricted data. - We demonstrate applications including transmission expansion potential, targeted line upgrades, and placement of large datacenter loads. 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 topology, electrical parameters, and geographic grounding. In most of the world, including the United States, realistic transmission-level…

Microsoft at NSDI 2026: Advances in large-scale networked systems

Large-scale networked systems underpin cloud computing, AI, and distributed applications and services. The USENIX Symposium on Networked Systems Design and Implementation 2026 (opens in new tab) (NSDI ’26) is a leading forum where researchers and practitioners share new research, insights, and advances in the design and operation of these systems. Microsoft is proud to support NSDI ’26 as a returning sponsor, reflecting our ongoing commitment to advancing systems and networking research and engaging with the broader community. Microsoft researchers and engineering leaders are also serving on the program committee and in other organizational roles. This year, 11 papers by Microsoft authors and collaborators were accepted to the conference, spanning datacenter and wide-area networks, AI systems, and cloud infrastructure. Together, they highlight advances in building and operating large-scale networked systems. Spotlight: Event Series Technical sessions Monday, May 4, 2:00–3:20 PM DroidSpeak:…

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

At a glance - Some risks appear only when agents interact, not when tested alone. Actions that seem harmless can cascade causing a chain reaction across an agent network. - In our tests, a single malicious message passed from agent to agent, extracting private data at each step and pulling uninvolved agents into the chain. - We saw early signs that some agent networks become more resistant to these attacks, but defenses are still an open challenge being worked on. Agents belonging to different users and organizations are beginning to interact with each other. These networks of agents are emerging as advances in large language models (LLMs) and silicon lower barriers to building agents, while tools like Claude, Copilot, and ChatGPT, along with existing platforms such as email and GitHub, bring them into constant contact. As a result, agents are…

AutoAdapt: Automated domain adaptation for large language models

At a glance - Problem: Adapting large language models to specialized, high-stakes domains is slow, expensive, and hard to reproduce. - What we built: AutoAdapt automates planning, strategy selection (e.g., RAG vs. fine-tuning), and tuning under real deployment constraints. - How it works: A structured configuration graph maps the full scope of the adaptation process, an agentic planner selects and sequences the right steps, and a budget-aware optimization loop (AutoRefine) refines the process within defined constraints. - Why it matters: The result is faster, automated, more reliable domain adaptation that turns weeks of manual iteration into repeatable pipelines. 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…

Can we AI our way to a more sustainable world?

Technical advancement is moving at such a rapid pace that it can be challenging to define the tomorrow we’re working toward. In The Shape of Things to Come, Microsoft Research leader Doug Burger and experts from across disciplines tease out the thorniest AI issues facing technologists, policymakers, business decisionmakers, and other stakeholders today. The goal: to amplify the shared understanding needed to build a future in which the AI transition is a net positive. In this episode, Burger is joined by Amy Luers, head of sustainability science and innovation at Microsoft, and Ishai Menache, an optimization researcher at Microsoft Research, to explore how AI can both contribute to and help address climate change, emphasizing the need to separate hype from data and understand its real impact. While datacenters account for a small share of global emissions, their rapid growth raises…

Ideas: Steering AI toward the work future we want

Behind every emerging technology is a great idea propelling it forward. In the Microsoft Research Podcast series Ideas, members of the research community at Microsoft discuss the beliefs that animate their research, the experiences and thinkers that inform it, and the positive human impact it targets. Since 2020, researchers across Microsoft have conducted, surfaced, and analyzed key research into how people work as part of the New Future of Work research initiative. They’ve done this through a variety of lenses—from changes caused by the pandemic to the adoption of hybrid work practices to the arrival of increasingly capable AI models—with the goal of empowering people and organizations to redefine work in real time. In this episode, Microsoft Chief Scientist and Technical Fellow Jaime Teevan talks with researchers Jenna Butler, Jake Hofman, and Rebecca Janssen about the latest efforts: the Microsoft…

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

At a glance - AI is driving rapid changes in the workplace, more sharply than those covered in previous editions of the New Future of Work - AI is changing how people work together, not just enabling them to work faster or from remote locations. Organizations that treat AI as a collaborative partner are seeing the biggest benefits. - The benefits of AI are not yet evenly distributed, underscoring the need for industry leaders to build AI that expands opportunity. The future is not predetermined. It will be shaped by the choices we make today. - Human expertise matters more, not less, in an AI-powered world. People are shifting from merely doing work to guiding, critiquing, and improving the work of AI. For the past five years, the New Future of Work report has captured how work is changing. This…

ADeLe: Predicting and explaining AI performance across tasks

At a glance - AI benchmarks report performance on specific tasks but provide limited insight into underlying capabilities; ADeLe evaluates models by scoring both tasks and models across 18 core abilities, enabling direct comparison between task demands and model capabilities. - Using these ability scores, the method predicts performance on new tasks with ~88% accuracy, including for models such as GPT-4o and Llama-3.1. - It builds ability profiles and identifies where models are likely to succeed or fail, highlighting strengths and limitations across tasks. - By linking outcomes to task demands, ADeLe explains differences in performance, showing how it changes as task complexity increases. 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.…

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

At a glance - VLM-based robot planners struggle with long, complex tasks because natural-language plans can be ambiguous, especially when specifying both actions and locations. - GroundedPlanBench evaluates whether models can plan actions and determine where they should occur across diverse, real-world robot scenarios. - Video-to-Spatially Grounded Planning (V2GP) is a framework that converts robot demonstration videos into spatially grounded training data, enabling models to learn planning and grounding jointly. - Grounded planning improves both task success and action accuracy, outperforming decoupled approaches in benchmark and real-world evaluations. 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…

AsgardBench: A benchmark for visually grounded interactive planning

At a glance - To successfully complete tasks, embodied AI agents must ground and update their plans based on visual feedback. - AsgardBench isolates whether agents can use visual observations to revise their plans as tasks unfold. - Spanning 108 controlled task instances across 12 task types, the benchmark requires agents to adapt their plans based on what they observe. - Because objects can be in different positions and states (e.g., clean or dirty), the same instruction can require different action sequences, even in the same environment. 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…

Will machines ever be intelligent?

Technical advances are moving at such a rapid pace that it can be challenging to define the tomorrow we’re working toward. In The Shape of Things to Come, Microsoft Research leader Doug Burger and experts from across disciplines tease out the thorniest AI issues facing technologists, policymakers, business decision-makers, and other stakeholders today. The goal: to amplify the shared understanding needed to build a future in which the AI transition is a net positive. In this first episode of the series, Burger is joined by Nicolò Fusi of Microsoft Research and Subutai Ahmad (opens in new tab) of Numenta to examine whether today’s AI systems are truly intelligent. They compare transformer-based large language models (LLMs) with the human brain’s distributed, continuously learning architecture, exploring differences in efficiency, representation, and sensory-motor grounding. The discussion probes what intelligence really means, where current…

Systematic debugging for AI agents: Introducing the AgentRx framework

At a glance - Problem: Debugging AI agent failures is hard because trajectories are long, stochastic, and often multi-agent, so the true root cause gets buried. - Solution: AgentRx (opens in new tab) pinpoints the first unrecoverable (“critical failure”) step by synthesizing guarded, executable constraints from tool schemas and domain policies, then logging evidence-backed violations step-by-step. - Benchmark + taxonomy: We release AgentRx Benchmark (opens in new tab) with 115 manually annotated failed trajectories across τ-bench, Flash, and Magentic-One, plus a grounded nine-category failure taxonomy. - Results + release: AgentRx improves failure localization (+23.6%) and root-cause attribution (+22.9%) over prompting baselines, and we are open-sourcing the framework and dataset. As AI agents transition from simple chatbots to autonomous systems capable of managing cloud incidents, navigating complex web interfaces, and executing multi-step API workflows, a new challenge has emerged: transparency. When…

PlugMem: Transforming raw agent interactions into reusable knowledge

At a glance - Today’s AI agents store long interaction histories but struggle to reuse them effectively. - Raw memory retrieval can overwhelm agents with lengthy, low-value context. - PlugMem transforms interaction history into structured, reusable knowledge. - A single, general-purpose memory module improves performance across diverse agent benchmarks while using fewer memory tokens. It seems counterintuitive: giving AI agents more memory can make them less effective. As interaction logs accumulate, they grow large, fill with irrelevant content, and become increasingly difficult to use. More memory means that agents must search through larger volumes of past interactions to find information relevant to the current task. Without structure, these records mix useful experiences with irrelevant details, making retrieval slower and less reliable. The challenge is not storing more experiences, but organizing them so that agents can quickly identify what matters in…