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Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field. In this conversation with Principal Applied Scientist Chad
At a glance End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States. Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity. Advance warning: The pipeline detected nearly 80% o
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback
AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the te
AI agents for therapeutic reasoning across biological contexts - Michelle M. Li, Carnegie Mellon University - Microsoft Research New England Generative Modeling & Sampling Seminar Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and pat
At a glance - The Flash family extends GigaPath and GigaTIME with dramatically improved efficiency, making large-scale pathology research more accessible and practical. - A distilled pathology foundation model backbone reduces computational requirements without sacrificing performance, enabling repeated analyses across larger patient cohorts. - These open models support population-scale discovery, helping researchers
A new artificial intelligence model designed to improve one of quantum chemistry’s most difficult calculations is now available through the CP2K software ecosystem, potentially allowing researchers to simulate larger molecular systems without sacrificing as much accuracy. Developed by Microsoft Research AI for Science, the model, known as Skala, replaces part of the conventional mathematical machinery used in density
Microsoft Research's Skala 1.1 demonstrates the continuous improvement of their deep-learning DFT approach. Trained on 2.5× more data than its predecessor, it delivers substantially higher accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction. Skala is now available in CP2K and is being integrated into Psi4, FHI-aims, ORCA, and VASP, brin
At a glance MindTopo is a new benchmark for testing topological reasoning in AI, evaluating whether multimodal models can understand concepts such as connectivity, enclosure, order, separation, and knots. The benchmark measures both reasoning and planning, testing not only whether models can recognize topological relationships in static images but also whether they can preserve and manipulate those relationships thro
Microsoft Research introduces CARE-X, a unified chest X-ray vision-language model (VLM) designed for diverse clinical interpretation tasks. It combines free-text generation with structured prediction to provide both narrative reports and deterministic outputs. The model uses reinforcement learning (DAPO) to reward clinical correctness in a multi-task setting. In a separate research experiment, the team paired Qwen3-V
Microsoft introduces Echoverse, a suite of twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds, each drilling a single control (e.g., date pickers, nested filters) in varied forms. Depth is key: these worlds replicate real application behavior, come seeded with realistic data, and maintain coherent state across screens and users. Training on all twelve nearly doubles a 9B
Microsoft Research introduces EvoLib, a framework that enables large language models to learn from their own experience during inference without ground-truth labels or external feedback. EvoLib transforms past attempts into reusable skills and reflective insights, continually refining, consolidating, and reweighting them to turn instance-specific observations into increasingly general knowledge over time. This evolvi
How Rust, Lean, Aeneas, and AI agents are helping scale formal verification for production cryptographic algorithms At a glance SymCrypt develops new verified cryptography using Rust, Aeneas, and Lean to provide higher security assurance. We prove that their code safely and correctly implements standard algorithms, notably for post-quantum cryptography. We are releasing verified code, specs, properties, and proofs in
At a glance - Aurora 1.5 is a major extension of Microsoft’s Aurora Earth System foundation model that adds 22 more weather variables relevant to energy, agriculture, transport, and climate risk, along with hourly temporal resolution and probabilistic ensemble forecasting. - Released as open source on GitHub with model checkpoints on Hugging Face, Aurora 1.5 enables researchers and developers to use, evaluate, and bu
Aurora 1.5: Fine-Tuning a Foundation Model for Medium-Range Ensemble Weather Prediction - Jonathan Weyn , - Zekun Ni , - Amit Misra , - Will Fein , - Haiyu Dong , - Wessel P. Bruinsma , - Richard E. Turner , - Matt Corey , - Kit Thambiratnam , - Kevin White , - Kenji Takeda We present Aurora 1.5, a fine-tuned variant of the Aurora atmospheric foundation model [Bodnar et al., 2025] optimized for skillful medium-range
Microsoft Research AI for Science introduces Flint, a visualization intermediate language for AI-driven chart creation. Flint enables AI agents to reliably generate expressive, visually polished charts from simple, human-editable specifications. It leverages semantic data types to guide the compiler in choosing appropriate scales, baselines, formatting, and color schemes, and automatically manages layout to adapt to
SkillOpt treats an agent's skill file as a trainable parameter outside a frozen target model, converting skill writing from one-shot prompting into a controlled optimization process. Across six benchmarks, seven target models, and three execution modes, SkillOpt achieves best or tied-best performance in all 52 evaluation cells, improving performance without updating model weights. The method keeps skills compact and
De novo Generation for Molecular Structure Elucidation from Mass Spectrometry - Runzhong Wang | MIT - Microsoft Research New England Generative Modeling & Sampling Seminar Recent advances in generative AI are enabling new approaches to scientific discovery in chemistry and biology. In this talk, we present DiffMS and FRIGID, two generative AI frameworks for de novo molecular structure elucidation from tandem mass spe