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We propose QGPINNs, a physics-informed neural network framework developed in PyTorch for the numerical solution of nonlocal differential equations on quantum graphs. The framework is designed as a gen

Vaibhav Mehandiratta, Saket Ramchandra

Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effects produce that saving. Routing force through a c

Nan Wang, Mohit Yadav, Jonathan Wulff

Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference. We develop a genera

Chengpiao Huang, Kaizheng Wang

We study the mixing time of weighted Dikin walks for sampling from exponential distributions on polytopes and truncated positive-semidefinite (PSD) cones. Our first result gives a general total-variat

Yuansi Chen, Yunbum Kook

We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent $α\geq 0$ for polynomial inner-product kernels. We derive asymptoticall

Lorenzo Rizzi, Arie Wortsman Zurich, Bruno Loureiro

Neural-network optimization in 2025-2026 is no longer well described as a succession of new Adam variants. The design space has expanded from coordinates to matrices and layers, from fixed training ho

Ruoran Xu

Modern agent systems assemble capabilities at runtime, and this dynamic composition has recently received a complete formal treat ment in the spatiotemporal-composability calculus, in which a capabili

Hanzhang Jia, Liheng Zeng, Hao Cheng

As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance, and yield simpler, more interpretable models. Ho

Kia Kazemi-Nia, Harsh Bandhey, Philip J. Freda

Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveraging off-the-shelf image diffusion models, they ei

Haosen Yang, Jifei Song, Zhensong Zhang

Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representation bias: systemati

Aaryan Ajay Sharma, Sai Nishanth Padala, Seganrasan Subramanian

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