Microsoft Research
Microsoft Research is Microsoft’s research organization, working across disciplines on scientific discovery and technology innovation, with a stated focus on challenges relevant to Microsoft and society.
Stochastic Control Closes the Sampling Loop for Rare-Event Analysis
Microsoft Research’s Yuanqi Du and Carles Domingo-Enrich recast rare-event simulation as a stochastic optimal control problem, arguing that the committor function at the center of Transition Path Theory can be learned by using each current estimate to steer new trajectories into the transition region. Their framework turns committor estimation into a feedback loop: a transformed value function induces a Doob-style control, that control generates more useful reactive samples, and the samples improve the estimate. They present REACT-VM, an off-policy Value Matching objective with a stated first-order optimality guarantee, as the more principled version of the method, and report stronger benchmark results than variational committor-learning baselines.
FRIGID Scales Molecular Structure Elucidation With Masked Diffusion
MIT postdoc Runzhong Wang argues that de novo molecular structure elucidation from tandem mass spectrometry is constrained less by instruments than by computation: researchers can produce high-quality spectra, but often cannot infer the molecules behind them. His talk presents DiffMS and FRIGID, two diffusion-based inverse models that decompose the task into spectrum-to-fingerprint prediction and scalable fingerprint-to-structure generation. Wang’s central claim is that scaling helps most where chemical structure data are abundant, while forward fragmentation models can guide inference by identifying parts of a generated molecule that do not match the observed spectrum.
Hard Constraints Steer Generative AI Toward Chemically Valid Materials
MIT PhD student Mouyang Cheng argues that generative models for materials discovery need explicit scientific constraints, not just larger diffusion models. In a Microsoft Research seminar, he describes two approaches: diffusion inpainting that forces generated crystals to contain target structural motifs, and CrysVCD, a valence-constrained framework that generates charge-balanced formulas before predicting structures. His case is that constraints such as motifs, valence and stability screens make generative materials design more useful in a field where data are sparse and chemically invalid samples are easy to produce.
Unified FHE Accelerator Targets Logic and SIMD Schemes on One Array
Minxuan Zhou of the Illinois Institute of Technology argues that fully homomorphic encryption will not become practical through cryptographic schemes alone, because its costs are dominated by ciphertext expansion, polynomial arithmetic, and data movement. In a Microsoft Research talk hosted by Patrick Longa, Zhou presents UFC, a unified FHE accelerator designed to support both logic and SIMD schemes by reducing their workloads to shared low-level primitives rather than building separate scheme-specific pipelines. The case for UFC is that hybrid FHE applications need both styles of computation, and that a common hardware substrate, NTT-centered interconnect, near-memory support, and compiler scheduling can outperform or avoid the inefficiencies of split accelerators.
Self-Consistent Interpolants Learn Clean Priors From Corrupted Data
Jiequn Han’s talk argues that transport-based generative models should be treated not only as tools for sampling clean data distributions, but as machinery for recovering and adapting those distributions when the usual clean training set is absent. His main proposal, Self-Consistent Stochastic Interpolants, learns a clean prior from corrupted observations by iterating a transport map until the learned distribution, passed through a trusted forward simulator, reproduces the observed data. Han presents the method as a black-box alternative to EM-style inverse generative modeling, with the caveat that simulator mismatch remains a central unresolved risk.
Flow Policies Need New Q-Learning Methods for Online Robot Adaptation
UC Berkeley PhD student Qiyang “Colin” Li argues that the flow-matching and diffusion policies now effective for robotic manipulation expose a weakness in standard Q-learning: they model complex, multimodal action chunks well, but are hard to optimize with the reparameterized actor gradients used in efficient continuous-control RL. He presents two approaches, Flow Q-learning and Q-learning with Adjoint Matching, as ways to make off-policy RL work with these policies while reusing prior robot data. The trade-off, in Li’s account, is between the stability gained by distilling flows into one-step actors and the expressivity preserved by keeping multistep flow policies.
Hamiltonian Flow Maps Learn Larger Molecular Dynamics Steps Without Trajectories
Michael Plainer, Winfried Ripken and Gregor Lied argue that generative models can attack molecular dynamics’ central bottleneck: the gap between femtosecond integration steps and biological processes that unfold many orders of magnitude later. In the Microsoft Research seminar, they separate the problem by timescale, using diffusion models to sample equilibrium Boltzmann states and extract force information, while proposing Hamiltonian flow maps for the intermediate regime where simulations need large, stable steps without training on expensive future-state trajectories.
Fixed-Point Bridge Matching Makes Diffusion Sampling Scalable Without Target Data
Lorenz Richter’s seminar argues for a non-Markovian route to diffusion-based sampling when the target distribution is known only through an unnormalized density rather than data. He presents existing Markovian path-space samplers as theoretically flexible but increasingly constrained by trajectory simulation and storage costs, then proposes building reciprocal bridge measures from endpoint couplings and learning their Markovian projection by fixed-point regression. The resulting Bridge Matching Sampler, Richter says, uses a single learned control, accommodates flexible priors and reference processes, and shows improved stability and mode preservation in high-dimensional synthetic and molecular benchmarks, especially with damping.
Denoising Markov Models Generalize Diffusion Through Reverse-Time Generators
Stanford Ph.D. candidate Yinuo Ren argues that diffusion, discrete diffusion, and broader jump-based generative models can be treated as instances of the same problem: choose a forward Markov process that carries data toward a simple reference law, then learn its reverse-time generator. His framework gives conditions under which that reverse generator is explicit up to unknown densities and turns the resulting approximation problem into a path-space KL objective via Doob’s h-transform. The payoff, Ren says, is a principled way to design denoising models beyond Gaussian diffusion, including discrete and Lévy-type dynamics.
Dynamic Measure Transport Needs New Rules for Density-Driven Sampling
Aimee Maurais argues that dynamic measure transport, now central to diffusion models and flow matching, needs different design principles when the target distribution is specified by densities, likelihoods, or prior samples rather than training data. In a Microsoft Research seminar, she presents three lines of work toward that goal: gradient-free particle dynamics using likelihood evaluations, PDE-constrained path design to avoid unstable interpolations, and localized transport velocities that exploit conditional-independence structure in high-dimensional Bayesian and data-assimilation problems.
Low Intrinsic Dimension Lets Blind Denoisers Track Implicit Diffusion Schedules
Aram-Alexandre Pooladian argues that blind denoising diffusion models can dispense with an explicit noise schedule because the noisy sample can reveal its own noise level when the data are low-dimensional inside a high-dimensional ambient space. In work with Zahra Kadkhodaie, Sinho Chewi, and Eero Simoncelli, he presents theory and experiments showing that such models can track an implicit reverse-process schedule and sample accurately in polynomially many steps as a function of intrinsic dimension. The empirical comparison suggests a further advantage: blind models may avoid finite-step mismatch between a prescribed schedule and the actual residual noise in the sample.
Energy-Based Fine-Tuning Improves Accuracy Without RLVR’s Validation-Loss Penalty
Mujin Kwun and Carles Domingo-Enrich present energy-based fine-tuning as a post-training method that replaces next-token imitation or task-specific rewards with sequence-level feature matching. Their argument is that supervised fine-tuning remains efficient but is trained under teacher forcing, while RL with verifiable rewards can improve accuracy without preserving the target completion distribution. EBFT instead samples model rollouts, compares their frozen-model feature embeddings with reference completions, and uses that signal for policy-gradient updates; in the reported coding and translation experiments, it matched or exceeded RLVR accuracy while producing lower validation cross-entropy than both RLVR and SFT.
Split-Flows Make Mapping Entropy Computable for Molecular Coarse-Graining
Tristan Bereau presents Split-Flows, a flow-based method for connecting atomistic and coarse-grained molecular representations by adding explicit noise variables for the degrees of freedom lost under coarse-graining. The argument is that this augmentation turns a many-to-one mapping into a tractable coordinate transform, enabling both generative backmapping and computation of configuration-dependent mapping entropy. Bereau says the approach makes information loss measurable for complex molecular systems, though it depends on a differentiable bijective construction and still faces scaling costs.
Meta Flow Maps Cut Reward-Alignment Costs With One-Step Posterior Sampling
Peter Potaptchik presents Meta Flow Maps as an amortized way to remove a costly inner loop in reward-aligning generative models: repeatedly simulating trajectories to estimate expected future reward from a noisy state. The method trains stochastic flow maps to produce differentiable, one-step samples from the clean-data posterior conditioned on any time and noisy state, enabling value-gradient estimates for inference-time steering and an off-policy objective for fine-tuning. In ImageNet experiments, Potaptchik argues, this lets a single-particle steered sampler outperform Best-of-1000 baselines across several rewards with far less compute.
Diffusion Models Generate Images Through Critical Instability Windows
Luca Ambrogioni argues that trained diffusion models generate images through brief instability windows rather than uniform step-by-step denoising. In a Microsoft Research generative modeling seminar, he links score dynamics, conditional entropy and statistical-physics phase transitions to show how low-frequency spatial modes soften at critical times, allowing noise to organize into coherent structure. Experiments on patch models, Fashion-MNIST and ImageNet models are presented as evidence that these critical windows govern both pattern formation and the timing of effective guidance.
Continuous Flow Models Can Be Simulated as Quantum Dynamics
David Layden, a staff research scientist at IBM Research, argues that trained continuous flow models can be recast as quantum simulation problems rather than merely classical samplers. In his account, the velocity field learned by a flow or diffusion-style model defines a Schrödinger equation whose solution is a quantum state encoding the model’s learned distribution. The result leaves training classical and theoretical, but claims that future quantum computers could provide coherent access to those distributions for downstream tasks such as Monte Carlo estimation, not just ordinary sampling.
Generative AI Targets Three Bottlenecks in One Health Decisions
Harvard postdoctoral fellow Lingkai Kong argues that generative AI can address three recurring failures in high-stakes One Health decision-making: scarce deployment data, hard-to-represent constrained policies, and shifting human priorities. In a Microsoft Research seminar, he presents flow matching, diffusion models and LLM agents as tools for patrol planning, poaching prediction, HIV testing policy and reward design, with collaborations involving conservation partners, the WHO, the Gates Foundation and South African health researchers.
Machine Learning Turns PDE Singularity Search Into Computer-Assisted Proof
Caltech applied math PhD candidate Yixuan Wang argues that high-precision computation can make singularity questions in nonlinear PDEs tractable only when it is tied to stability analysis and rigorous verification. In a Microsoft Research seminar on Navier-Stokes blowup and weak-solution nonuniqueness, Wang presents machine-learning tools such as PINNs, neural operators, and Kolmogorov–Arnold Networks as ways to discover candidate singular structures, not as substitutes for proof. His broader case is that numerics, analytical a posteriori estimates, and interval-certified computation must work together if singularities in systems such as Navier-Stokes are to be identified and verified.
Wavelet Score Models Show Local Interactions Drive Diffusion Denoising
Emma Finn argues that the memorization puzzle in diffusion models can be probed by replacing a black-box score network with an analytically solvable wavelet parameterization. In her Microsoft Research New England seminar, Finn presents the method as a way to isolate which data moments and dependency structures matter across noise scales. Her reported experiments on MNIST suggest that local same-scale wavelet interactions improve denoising more consistently than independent coefficient models or orientation-only coupling, while the larger question of whether the framework explains generative novelty remains unresolved.
Energy-Based Fine-Tuning Trains Language Models on Whole Responses
Microsoft Research’s presentation on energy-based fine-tuning argues that language-model post-training can be aimed at whole responses rather than next-token imitation. Carles Domingo-Enrich presents EBFT as a middle path between supervised fine-tuning and reinforcement learning: it samples model completions, compares them with ground-truth answers in a model-derived feature space, and turns that comparison into a policy-gradient update without a separate reward model or verifier. The reported results show gains over SFT on several coding and translation measures, with performance often comparable to RLVR while avoiding explicit correctness rewards.
AI’s Biggest Disruption Requires Rebuilding Markets Around Agents
David Rothschild argues that AI’s largest economic effects will come less from better models than from whether workflows and markets are rebuilt for agents rather than humans. In his Microsoft Research Forum talk and related work on agentic markets, he says the key question is architectural: open systems could reduce communication friction and spread welfare gains, while closed platforms could use the same capabilities to reinforce incumbency. The transition, in his account, depends on choices about delegation, monitoring, auditability, and market access that are being made before the full disruption is visible.
Interwhen Verifies AI Agent Actions Before They Become Irreversible
Microsoft Research’s Amit Sharma presents Interwhen as a framework for moving AI agents from post-hoc checking to verified execution while they are still acting. The open-source library uses LLMs to turn natural-language instructions, policies, and partial responses into smaller verifiable properties, then applies symbolic or model-based verifiers to tool calls and intermediate behavior. Sharma argues that this lets agents continue normally when checks pass but interrupts them when a verifier detects a violation, addressing risks that final-output review may catch too late.
GitHub Agentic Workflows Turn Actions Into AI-Run Development Processes
Microsoft Research’s Peli Halleux and Yash Lara present GitHub Agentic Workflows as a move from AI-assisted coding to repository-level process automation. Their argument is that agents should be embedded inside GitHub Actions to research, plan, assign, and open pull requests under human review, rather than operate as unconstrained swarms. The system’s promised scale depends on orchestration, sandboxing, limited permissions, and Microsoft-hosted models on Azure.
MagenticLite Brings Full Agent Workflows to Small Language Models
Microsoft Research is presenting MagenticLite as a full-stack agentic system designed to make small language models usable for multi-step work across a browser and local files. Weili Shi, Harkirat Behl and Hussein Mozannar argue that the capability comes from specializing the stack rather than relying on frontier-scale models: MagenticBrain handles planning, coding and delegation, while Fara 1.5 controls the browser. The release also emphasizes user oversight, with the agent pausing for credentials, approvals or other points where the user needs to take control.
Language-Agnostic Analysis Finds 15 Vulnerabilities in ZKP Projects
Arman Kolozyan of the Max Planck Institute for Security and Privacy argues that many zero-knowledge proof bugs arise from a mismatch between what a prover’s computation can produce and what a verifier’s constraints will accept. Presenting the paper “Language-Agnostic Detection of Bugs in Zero-Knowledge Proof Programs,” he describes a formal model, the Domain Consistency Model, and a static-analysis tool, CCC-Check, designed to detect those mismatches across ZKP languages. The work reports two-orders-of-magnitude speedups over SMT-based approaches on benchmarks and 15 previously unknown vulnerabilities in six ZKP projects, most of them outside the reach of existing models.