Boston Children’s uses AI to unlock new diagnoses
Boston Children’s Hospital uses OpenAI technology to improve patient care, reduce operational burden, and help diagnose more than 40 rare disease cases.
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Schr”odinger bridge problem via empirical risk minimization
arXiv:2602.08374v1 Announce Type: new Abstract: We study the Schr”odinger bridge problem when the endpoint distributions are available only through samples. Classical computational approaches estimate Schr”odinger potentials via Sinkhorn iterations on empirical measures and then construct a time-inhomogeneous drift by differentiating a kernel-smoothed dual solution. In contrast, we propose a learning-theoretic route: we rewrite the Schr”odinger system in terms of a single positive transformed potential that satisfies a nonlinear fixed-point equation and estimate this potential by empirical risk minimization […]
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From Tool Orchestration to Code Execution: A Study of MCP Design Choices
arXiv:2602.15945v1 Announce Type: new Abstract: Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments. As MCP-based systems scale to incorporate larger tool catalogs and multiple concurrently connected MCP servers, traditional tool-by-tool invocation increases coordination overhead, fragments state management, and limits support for wide-context operations. To address these scalability challenges, recent MCP designs have incorporated code execution as a first-class capability, an approach called Code Execution MCP […]
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Don’t Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis
arXiv:2601.02447v1 Announce Type: new Abstract: Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned retina. Most learning-based methods circumvent the problems arising from the anisotropy by using 2D approaches rather than performing volumetric analyses. These approaches inherently bear the risk of generating inconsistent results for neighboring B-scans. For example, 2D retinal layer segmentations can have irregular surfaces in 3D. Furthermore, the […]
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A Reproducible Kernel-Damping Evidence Protocol for SORT-AI Core-3 Structural Coupling Regimes
This technical note introduces a reproducible kernel-damping evidence protocol for the SORT-AI Core-3 applications AI.01 (Interconnect Stability Control), AI.04 (Runtime Control Coherence), and AI.13 (Agentic System Stability). These applications span complementary structural coupling regimes in advanced AI systems: physical/interconnect coupling, logical/runtime-control coupling, and semantic/agentic coupling. The protocol evaluates whether declared structural risk-transition scenarios admit a Gaussian kernel-damping reconstruction under the declared canonical SORT scale parameter σ 0 = 0.00190643. The analysis is restricted to the structural analysis layer […]
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Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
arXiv:2603.05149v1 Announce Type: cross Abstract: Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares […]
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Top 7 Coding Models You Can Run Locally in 2026
Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.
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EvoMU: Evolutionary Machine Unlearning
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material). A prominent approach is to fine-tune an existing model with an unlearning loss that retains overall utility. The space of suitable unlearning loss functions is vast, making the search for an optimal loss function daunting. Additionally, there might not even exist a universally optimal loss function: differences in the structure and overlap of the forget and retain data can cause a loss to work well […]
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Under anti-vaccine RFK Jr., CDC slashes childhood vaccine schedule
Under anti-vaccine Health Secretary Robert F. Kennedy Jr., federal health officials on Monday announced a sweeping and unprecedented overhaul of federal vaccine recommendations, abruptly paring down recommended immunizations for children from 17 to 11. Officials claimed the rationale for the change was to align US vaccine recommendations more closely with those of other high-income countries, namely Denmark, a small, far less diverse country of around 6 million people (smaller than the population of New York City) that has […]
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Hierarchic Flows to Estimate and Sample High-dimensional Probabilities
arXiv:2405.03468v2 Announce Type: replace Abstract: Finding low-dimensional interpretable models of complex physical fields such as turbulence remains an open question, 80 years after the pioneer work of Kolmogorov. Estimating high-dimensional probability distributions from data samples suffers from an optimization and an approximation curse of dimensionality. It may be avoided by following a hierarchic probability flow from coarse to fine scales. This inverse renormalization group is defined by conditional probabilities across scales, renormalized in a wavelet basis. For a […]
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