Telecomunicaciones, información y comunicación

Breakeven demonstration of quantum low-density parity-check codes

- High-rate quantum low-density parity-check (qLDPC) codes are a leading candidate for fault-tolerant quantum computing. They feature higher encoding rates than planar alternatives such as the surface...

Multiple Quantum Hypothesis Testing: One-Shot Pairwise Bounds and Sharp Asymptotics

- We consider Bayesian discrimination among multiple quantum states and establish a dimension-free one-shot upper bound on the minimum probability of error in terms of the sum of pairwise errors. This...

Quantum Algorithms for Triangle Cut Sparsification

- Triangles capture higher-order structures in graphs and are fundamental to applications such as clustering and network analysis.To enable efficient use of such structures at scale, we study the...

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

- Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise...

Quantum enhanced rare event discovery and sampling

- Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremely small probability. Efficiently discovering and...

Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

- Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations. However, existing paired topological divergences (e.g., RTD) are limited by heuristic...

Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction

- Vessel trajectory prediction from Automatic Identification System (AIS) data is essential for maritime situational awareness, yet it remains challenging due to irregular sampling, missing reports,...

LatentWave: JEPA Pretraining for Wireless Foundation Models

- Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruction, which can bias...

Anchor PCA

- Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal components...

Drag reduction or reward hacking? Recurrent multi-agent reinforcement learning that earns its reward

- A reinforcement-learning agent maximises its reward, which can diverge from the outcome its designer intended. In physical control the reward rarely closes that gap, and drag reduction in wall...

Finding Most Influential Sets

- Identifying most influential sets (MIS) - size-$k$ subsets whose removal maximally changes a target estimand - is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. For...

EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning

- Neural network (NN)-based nonlinear causal discovery methods recover DAG structure but leave each causal mechanism as a black box. Waxman et al. argued that extracting causal mechanisms from NN...

Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks

- Physics-Informed Neural Networks inherently suffer from task interference because they rely on a shared parameter space to satisfy both governing differential equations and boundary conditions. We...

$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences

- We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is...

Network model selection: A review of methods

- Understanding the processes behind the evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which...

PC Layer: Polynomial Weight Preconditioning for Improving LLM Pre-Training

- We propose a preconditioning (PC) layer, a weight parameterization via polynomial preconditioner that ensures stable weight conditioning throughout LLM training. The PC module reshapes the...

MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

- Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key...

Self-Augmenting Retrieval for Diffusion Language Models

- Discrete diffusion language models generate text by iteratively denoising an entire response in parallel. At each step, they predict tentative tokens for every masked position, committing the...

RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

- Recent advancements in reasoning language models have been driven by Reinforcement Learning (RL) fine-tuning. Most often, these rely on the Group Relative Policy Optimization (GRPO) algorithm or...

Complexity-Balanced Diffusion Splitting

- Standard continuous-time generative models rely on monolithic architectures that must navigate vastly different signal regimes, from isotropic noise to intricate data distributions. While scaling...

Pretraining Recurrent Networks without Recurrence

- Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential...