arXiv:2602.11234v1 Announce Type: new Abstract: Accurate prognosis for Glioblastoma (GBM) using deep learning (DL) is hindered by extreme spatial and structural heterogeneity. Moreover, inconsistent MRI acquisition protocols across institutions hinder generalizability of models. Conventional transformer and DL pipelines often fail to capture the multi-scale morphological diversity such as fragmented necrotic cores, infiltrating margins, and disjoint enhancing components leading to scanner-specific artifacts and poor cross-site prognosis. We propose TopoGBM, a learning framework designed to capture heterogeneity-preserved, scanner-robust representations from […]

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Ladybird adopts Rust, with help from AI Really interesting case-study from Andreas Kling on advanced, sophisticated use of coding agents for ambitious coding projects with critical code. After a few years hoping Swift’s platform support outside of the Apple ecosystem would mature they switched tracks to Rust their memory-safe language of choice, starting with an AI-assisted port of a critical library: Our first target was LibJS , Ladybird’s JavaScript engine. The lexer, parser, AST, and bytecode generator are […]

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arXiv:2602.20856v1 Announce Type: cross Abstract: This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net […]

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arXiv:2604.17670v1 Announce Type: cross Abstract: We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate […]

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arXiv:2603.22301v1 Announce Type: new Abstract: Large Language Models (LLMs) perform internal computations in continuous vector spaces yet produce discrete tokens — a fundamental mismatch whose geometric consequences remain poorly understood. We develop a mathematical framework that interprets LLM hidden states as points on a latent semantic manifold: a Riemannian submanifold equipped with the Fisher information metric, where tokens correspond to Voronoi regions partitioning the manifold. We define the expressibility gap, a geometric measure of the semantic distortion from […]

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Every AI-powered iOS app starts with the same assumption: “We’ll just call the AI API and show the result.” That assumption works — right up until real users, real devices, and real constraints enter the system. AI demos look simple. Production AI on iOS is not. This article is about what breaks when AI is treated as a regular API call — and what iOS engineers inevitably learn when shipping AI-powered features to real users. The Mental Model […]

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Modern businesses run on digital trust. Customers expect their data to be safe, and partners expect operations to be reliable. To meet those expectations, security must be a daily practice, not a yearly project. This article walks through practical measures that reduce risk fast. Each section focuses on actions you can apply in most environments. The goal is simple – shrink the attack surface and strengthen your defences without adding needless complexity. Assessing Your Risk Landscape Start with […]

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arXiv:2604.15320v1 Announce Type: new Abstract: How do we assess people’s abilities to interact with data visualizations? The current state-of-the-art visualization literacy tests — such as VLAT and its derivatives — only involve the use of static visualizations. Despite advances in investigating multiple visualization abilities, we do not yet have formal methods to assess the ability of a person to interact with a data visualization effectively. In this position paper, we discuss related literacy concepts and assessments to propose […]

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arXiv:2509.19189v4 Announce Type: replace-cross Abstract: Scaling laws have emerged as a unifying lens for understanding and guiding the training of large language models (LLMs). However, existing studies predominantly focus on the final-step loss, leaving open whether the entire loss dynamics obey similar laws and, crucially, how the learning rate schedule (LRS) shapes them. We address these gaps in a controlled theoretical setting by analyzing stochastic gradient descent (SGD) on a power-law kernel regression model. The key insight is […]

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arXiv:2601.19186v2 Announce Type: replace Abstract: Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focuses on supervised learning, fairness in policy learning remains less explored. Because policy learning is interventional, it induces two distinct fairness targets: action fairness (equitable action assignments) and outcome fairness (equitable downstream consequences). Crucially, equalizing actions does not generally equalize outcomes when groups face different constraints or respond differently to […]

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