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arXiv:2511.20376v2 Announce Type: replace Abstract: We study efficient algorithms for recovering cliques in dense random intersection graphs (RIGs). In this model, $d = n^{\Omega(1)}$ cliques...
arXiv:2511.21277v2 Announce Type: replace Abstract: This paper presents ChronoRAN, a mathematical framework for accurately computing one-way latency (for uplink and downlink) in the 5G RAN...
arXiv:2511.23152v2 Announce Type: replace Abstract: While modern neural architectures typically generalize via smooth interpolation, it lacks the inductive biases required to uncover algebraic structures essential...
arXiv:2512.01115v3 Announce Type: replace Abstract: We study the design of a privatization mechanism and privacy accounting in the Pufferfish Privacy (PP) family. Specifically, motivated by...
arXiv:2512.01249v2 Announce Type: replace Abstract: This paper introduces a new family of multi-parent recombination operators for Genetic Algorithms (GAs), based on normalized Pascal (binomial) coefficients....
arXiv:2512.01841v2 Announce Type: replace Abstract: This work presents error analysis for a finite element method applied to a two-dimensional singularly perturbed convection-diffusion turning point problem....
arXiv:2512.02213v2 Announce Type: replace Abstract: Effective text generation and chat interfaces for low-resource languages (LRLs) remain a challenge for state-of-the-art large language models (LLMs) to...
arXiv:2512.03298v2 Announce Type: replace Abstract: Regime transitions routinely break stationarity in time series, making calibrated uncertainty as important as point accuracy. We study distribution-free uncertainty...
arXiv:2512.03520v2 Announce Type: replace Abstract: We present FloodDiffusion, a new framework for text-driven, streaming human motion generation. Given time-varying text prompts, FloodDiffusion generates text-aligned, seamless...
arXiv:2512.04904v2 Announce Type: replace Abstract: Despite tremendous recent progress, Flow Matching methods still suffer from exposure bias due to discrepancies in training and inference. This...
arXiv:2512.05089v5 Announce Type: replace Abstract: Real-world phenomena do not generate arbitrary variability: their signals concentrate on compact, low-variability subsets of functional space, enabling rapid generalization...
arXiv:2512.05790v4 Announce Type: replace Abstract: We develop a theoretical framework that explains how gating mechanisms determine the learnability window $\mathcal{H}_N$ of recurrent neural networks, defined...
arXiv:2512.07010v3 Announce Type: replace Abstract: Layer-wise Relevance Propagation (LRP) provides principled attribution for neural networks through conservation properties and foundations in Deep Taylor Decomposition. However,...
arXiv:2512.09374v3 Announce Type: replace Abstract: A language is said to be in catalytic logspace if we can test membership using a deterministic logspace machine that...
arXiv:2512.10524v2 Announce Type: replace Abstract: A pre-trained unconditional diffusion model, combined with posterior sampling or maximum a posteriori (MAP) estimation techniques, can solve arbitrary inverse...
arXiv:2512.11585v2 Announce Type: replace Abstract: Traditional measures of closeness and betweenness centrality in networks rely on the shortest paths between nodes. Many standard metrics fail...
arXiv:2512.12307v3 Announce Type: replace Abstract: While deep learning methods have achieved impressive success in many vision benchmarks, it remains difficult to understand and explain the...
arXiv:2512.12783v3 Announce Type: replace Abstract: Financial exclusion constrains entrepreneurship, increases income volatility, and widens wealth gaps. Underbanked consumers in Istanbul often have no bureau file...
arXiv:2512.14253v2 Announce Type: replace Abstract: In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support both...
arXiv:2512.15922v2 Announce Type: replace Abstract: Despite initial successes and a variety of architectures, retrieval-augmented generation systems still struggle to reliably retrieve and connect the multi-step...
arXiv:2512.16956v2 Announce Type: replace Abstract: Retrieving code functions, classes or files that are relevant in order to solve a given user query, bug report or...
arXiv:2512.17043v2 Announce Type: replace Abstract: Knowledge Graph Question Answering (KGQA) has largely focused on entity-centric queries that return a single answer entity. However, many real-world...
arXiv:2512.17058v2 Announce Type: replace Abstract: We establish the last missing link allowing to describe those complete separable metric spaces $X$ in which the $k$ nearest...
arXiv:2512.17873v2 Announce Type: replace Abstract: Standard diffusion models (DMs) rely on the total destruction of data into non-informative white noise, forcing the backward process to...