arXiv:2601.03654v2 Announce Type: replace Abstract: In this study, we introduce a quantum computing method that incorporates Ridglet transforms into quantum processing pipelines for financial time-series forecasting with Quantum Approximate Optimization Algorithm (QAOA)-based portfolio optimization. We propose a Quantum Ridgelet Neural…
arXiv:2604.26097v1 Announce Type: new Abstract: Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs generalize readily to arbitrary shapes, mesh topologies, and material parameters, existing architectures struggle…
arXiv:2604.14246v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing long-tail knowledge. We identify that this fragility stems from static Top-$k$ routing: routers tend to favor high-frequency patterns…
arXiv:2604.26130v1 Announce Type: new Abstract: Every RLHF-trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit — logit lens, direct logit attribution, activation patching, sparse autoencoders — was built for generative LLMs whose primitives all project…
arXiv:2309.13722v3 Announce Type: replace-cross Abstract: Recently, several deep learning (DL) methods for approximating high-dimensional partial differential equations (PDEs) have been proposed. The interest that these methods have generated in the literature is in large part due to simulations which appear…
arXiv:2604.26133v1 Announce Type: new Abstract: Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement characteristics with environmental health outcomes. We develop a data-driven framework to assess heat vulnerability in Rio…
arXiv:2512.18365v2 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches…
arXiv:2604.26169v1 Announce Type: new Abstract: Treatment allocation under budget constraints is a central challenge in digital advertising: advertisers must decide which users to show ads to while spending a limited budget wisely. The standard approach follows a two-stage offline pipeline…
arXiv:2604.15238v2 Announce Type: replace-cross Abstract: This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce a nonlinear separation principle that guarantees global exponential stability for the…
arXiv:2604.26173v1 Announce Type: new Abstract: An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existing selection methods often…
