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Near-Optimal Mixed Strategy for Zero-Sum Linear-Quadratic Differential Games
Announce Type: cross Abstract: Deriving analytic solutions for optimal mixed strategies in zero-sum linear-quadratic differential games (ZSLQDGs) remains an open problem. In this paper, we analytically synthesize near-optimal mixed strategies for ZSLQDGs and establish rigorous performance certifications. Specifically, we construct a surrogate pure-strategy stochastic differential game (SDG) by matching the first two moments of the mixed strategies.
After remarkable progress, newborn, child, and adolescent survival is now at risk
In 2024, 4.9 million children under the age of 5 years died.1 When compared to the loss of 10.1 million children under 5 years old in 2000, this reduction is a remarkable achievement in a relatively short period of time. However, progress in reducing child mortality has slowed since 2015.A new series of estimates published in The BMJ show that based on current trends, 60 countries are unlikely to meet the United Nations Sustainable Development Goal (UN SDG) targets for newborn and child...
Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback
arXiv:2606.06113v1 Announce Type: new Abstract: Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still...
SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
Announce Type: new Abstract: Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat ("reconstruction", the recovery of an individual's hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings. We present the first systematization of reconstruction (equivalently, attribute inference) attacks on...
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
Announce Type: replace Abstract: Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global...
Nvidia Cosmos 3
Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks. NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning, world generation, and action generation within a single open model.