arXiv:2602.02500v1 Announce Type: new Abstract: The Newton-Schulz (NS) iteration has gained increasing interest for its role in the Muon optimizer and the Stiefel manifold. However, the conventional NS iteration suffers from inefficiency and instability. Although various improvements have been introduced to NS iteration, they fail to deviate from the conventional iterative paradigm, which could increase computation burden largely due to the matrix products along the long dimension repeatedly. To address this, we consolidate the iterative structure into a […]

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Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning (RL) approaches model LLM agents as flat policies operating at a single time scale, selecting one action at each turn. In sparse-reward settings, such flat policies must propagate credit across the entire trajectory without explicit temporal abstraction, which often leads to unstable optimization […]

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I was doing some research today and ran into a couple instances where part of one law was copied and pasted verbatim into another law. I suppose this is not uncommon, but I’m not a lawyer, so I don’t have that much experience comparing laws. I do, however, consult for lawyers and have to look up laws from time to time. Here’s an example from California Health and Safety Code § 1385.10 and the California Insurance Code § […]

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arXiv:2602.10587v1 Announce Type: new Abstract: In this work, we propose a novel deep bootstrap framework for nonparametric regression based on conditional diffusion models. Specifically, we construct a conditional diffusion model to learn the distribution of the response variable given the covariates. This model is then used to generate bootstrap samples by pairing the original covariates with newly synthesized responses. We reformulate nonparametric regression as conditional sample mean estimation, which is implemented directly via the learned conditional diffusion model. […]

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arXiv:2602.12489v1 Announce Type: new Abstract: We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-level content navigation, where the goal is to localize specific 2D slices within a 3D volume or determine the anatomical coverage of a 3D scan based on its 2D slices. This serves as an important preprocessing step for various diagnostic tasks, as well as for automatic registration and segmentation pipelines. Our approach […]

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arXiv:2603.24626v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) is inherently affected by sparsity caused by dropout events, in which expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and can compromise downstream analyses. Numerous imputation methods have been proposed to address this, and these methods encompass a wide range of approaches from traditional statistical models to recently developed deep learning (DL)-based methods. However, their comparative performance remains unclear, as existing […]

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Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Hi, everyone — Colin Lecher here, an investigative reporter at The Markup. Back in March, we broke some news about a small-business advice chatbot being piloted by the New York City government. The bot — available at chat.nyc.gov — was meant to be an AI-powered, one-stop shop for entrepreneurs […]

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arXiv:2603.25778v1 Announce Type: new Abstract: Endoscopic video analysis is essential for early gastrointestinal screening but remains hindered by limited high-quality annotations. While self-supervised video pre-training shows promise, existing methods developed for natural videos prioritize dense spatio-temporal modeling and exhibit motion bias, overlooking the static, structured semantics critical to clinical decision-making. To address this challenge, we propose Focus-to-Perceive Representation Learning (FPRL), a cognition-inspired hierarchical framework that emulates clinical examination. FPRL first focuses on intra-frame lesion-centric regions to learn static […]

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Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data). While global aggregation strategies promote broad generalization, they often fail to capture the diversity of local data distributions, leading to suboptimal personalization. We address this problem with FedPrism, a framework that uses two main strategies. First, it uses a Prism Decomposition method that builds each client’s model from three parts: a global foundation, a shared group part for […]

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