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17,191 papers
This work presents an evidence-grounded agentic AI framework that connects liver and lesion segmentation with deterministic quantification and structured reporting, taking a step toward human-centered medical AI that prioritizes traceability and clinical relevance.
A coding agent can migrate a machine learning workload, report success, and still change how the model trains. Rising compute costs, limited accelerator capacity, and vendor dependence are pushing organizations toward alternatives to graphics processing units (GPUs); a key question is whether agent-generated migrations...
Semantic communication jointly optimizes inference accuracy, channel use, and computation, yet the underlying communication program is typically specified before training. This paper presents ComRSI (Communications-Oriented Recursive Self-Improvement), a framework for recursively evolving that program through large-lan...
Field-boundary accuracy depends on the model, sensor resolution, label discretization, and evaluation procedure. Measuring the contribution of sensor resolution therefore requires a controlled comparison across sensors. We present Fields of the Planet (FTP), a 3 m PlanetScope companion to Fields of The World (FTW) cont...
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Soft Mixture-of-Recursions (SoftMoR) is proposed, which learns token-wise weights to combine representations across recursion steps, and instantiate it as the Soft Recursive Vision Transformer (SR-ViT), which can benefit from deeper recursion while adding few parameters.
Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficult to certify. This paper develops a no-harm certification-and-selection framework for physics-informed inverse learning. A learned reconstruction is accepted only when it...
This work provides a deterministic dynamic stepsize schedule that accelerates the convergence rate of EG to $\mathcal{O}(T^{-2/3+\varepsilon})$ for any $\varepsilon>0$ and shows that this rate is nearly tight when the extrapolation and update steps of EG use the same stepsize.
Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavailable at inference time-the so called unawareness setting, principled methods for obtaining accurate predictions under relaxed fairness constraints are largely missing. In...
We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of $K$ items from a ground set of size $N$ and observes single-choice feedback. Unlike linear or generalized linear bandits, MNL bandits have a combinatorial action space, which makes classical optimal d...
This work introduces a framework that decouples agent identity from behavior, capturing a continuous manifold from which agents instantiate their behaviors in response to events, and proves that this construction ensures that diversity does not interfere with reward maximization by design.
Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models. In this paper, we study the evolution of tokens in deep encoder-only transformers at inference time which is described in the large-token limit by a mean-field continuit...
Autonomous drone fleets have potential in medical supply delivery during disaster incident response. However, coordinating multiple drones in such settings introduces compounding challenges: dynamic hazards such as wind, obstacles, and intermittent connectivity, constrained energy budgets, and the need to serve patient...
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
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