Knowledge catalogue
arXiv:2506.01529v2 Announce Type: replace Abstract: Learning meaningful abstract models of Markov Decision Processes (MDPs) is crucial for improving generalization from limited data. In this work, we
arXiv:2605.19074v1 Announce Type: cross Abstract: The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting mode
arXiv:2605.19959v1 Announce Type: new Abstract: Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear a
arXiv:2602.04998v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent
arXiv:2504.00470v2 Announce Type: replace-cross Abstract: To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of exis
arXiv:2605.19060v1 Announce Type: cross Abstract: High-resolution 3D medical image generation remains challenging because fully volumetric models are computationally expensive, while efficient 2D slic
arXiv:2605.19811v1 Announce Type: new Abstract: In large-scale optimization, the cheapness and effectiveness of update steps are the most crucial factors for a successful optimizer. Sign-based optimiz
arXiv:2605.19714v1 Announce Type: new Abstract: Investor sentiment shapes financial markets, yet modeling sentiment in Arabic financial contexts remains challenging due to linguistic complexity and li
arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r
arXiv:2605.19018v1 Announce Type: new Abstract: Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning
arXiv:2605.18829v1 Announce Type: new Abstract: Frontier commercial generative models face a growing threat from distillation, whereby a distiller harvests generated responses and trains a competing m
arXiv:2605.18846v1 Announce Type: cross Abstract: Classical communication systems fail not only through random noise but also when transmitter and receiver use incompatible operational codebooks. Vari
arXiv:2605.19274v1 Announce Type: new Abstract: LLMs deployed multilingually are often audited via English explanations for non-English inputs. We evaluate extractive explanations ''where the model id
arXiv:2605.18774v1 Announce Type: cross Abstract: In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true
arXiv:2509.14839v2 Announce Type: replace Abstract: City administrations increasingly rely on comprehensive databases and urban digital twins of city assets, such as traffic signs and trees, as well a
arXiv:2605.18931v1 Announce Type: cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling. This behavior poses a fundamental challenge fo
arXiv:2605.19305v1 Announce Type: cross Abstract: This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be
arXiv:2605.19386v1 Announce Type: new Abstract: Reconstructing simulation-ready deformable objects is important for vision, graphics, and robotics. Existing physics-driven methods can recover physical
arXiv:2512.05958v2 Announce Type: replace-cross Abstract: Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information provi
arXiv:2605.19594v1 Announce Type: new Abstract: Navigating to instance-level targets in complex environments is a challenging problem. Many existing zero-shot methods achieve strong performance by mod
arXiv:2605.20090v1 Announce Type: new Abstract: Multi-modal remote sensing images are vital for Earth observation, yet complete paired observations are often scarce in practice. Existing generative me
arXiv:2605.19619v1 Announce Type: cross Abstract: Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon
arXiv:2605.20043v1 Announce Type: new Abstract: We present an orthography-aware error analysis of Japanese past-tense morphological inflection, treating hiragana not merely as a transcriptional medium
arXiv:2605.19768v1 Announce Type: new Abstract: We study reinforcement learning for episodic Markov Decision Processes (MDPs) whose transitions are modelled by a multinomial logistic (MNL) model. Exis
arXiv:2605.19304v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction, it suffers from significant overhead due to massive redundant primitives. Exist
arXiv:2605.18869v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across a wide range of tasks but are highly sensitive to prompt design, motivating the need fo
arXiv:2412.13111v2 Announce Type: replace Abstract: Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry. Existing methods of
arXiv:2605.19371v1 Announce Type: cross Abstract: Diffusion models are widely used in image generation, with most relying on noise-based corruption and denoising. A distinct branch instead uses blur a
arXiv:2605.18884v1 Announce Type: cross Abstract: Multimodal emotion recognition aims to integrate text, audio, and video sources to understand human affective states. Although multimodal large langua
arXiv:2507.06428v2 Announce Type: replace-cross Abstract: We mathematically analyze and numerically study an actor-critic machine learning algorithm for solving high-dimensional Hamilton-Jacobi-Bellma
arXiv:2603.24400v2 Announce Type: replace-cross Abstract: We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the ac
arXiv:2605.19104v1 Announce Type: cross Abstract: Continuum robots enable dexterous manipulation in constrained environments, but require accurate and efficient models for real-time manipulation and c
arXiv:2605.17326v1 Announce Type: cross Abstract: We investigate the role of the noise schedule in diffusion processes on Lie groups, with particular emphasis on applications to lattice gauge theory.
arXiv:2605.19965v1 Announce Type: new Abstract: Blind source separation (BSS) is a natural framework for studying how latent causes may be recovered from sensory mixtures, but deriving online and biol
arXiv:2605.19584v1 Announce Type: new Abstract: We study an online market-making problem in which a learner sequentially posts bid and ask prices for a single asset while interacting with traders hold
An OpenAI general-purpose reasoning model autonomously disproved a conjecture posed by Paul Erdos in 1946, overturning 80 years of mathematical belief. The breakthrough concerns the planar unit distan
arXiv:2605.19769v1 Announce Type: new Abstract: We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four
arXiv:2605.20122v1 Announce Type: cross Abstract: Squared Wasserstein distance is a frequently used tool to measure discrepancy between probability distributions. This distance is typically computed b
arXiv:2605.19032v1 Announce Type: new Abstract: Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via
arXiv:2605.20185v1 Announce Type: cross Abstract: Existing Gaussian avatar methods typically parameterize geometry on a body-template surface, which entangles the avatar's representation space with th
arXiv:2605.19185v1 Announce Type: cross Abstract: Sparse goal-conditioned planning with few cost-to-go labels can be viewed as a graph-PDE Dirichlet extension problem: extend sparse labels on a goal-d
arXiv:2605.19145v1 Announce Type: new Abstract: In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learni
arXiv:2605.19943v1 Announce Type: new Abstract: Tiny Recursive Models (TRM) solve complex reasoning tasks with a fraction of the parameters of modern large language models (LLMs) by iteratively refini
arXiv:2605.20079v1 Announce Type: cross Abstract: Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. H
arXiv:2605.19721v1 Announce Type: new Abstract: Graph combinatorial optimization (GCO) has attracted growing interest, as many NP-hard problems naturally admit graph formulations, yet their combinator
arXiv:2605.19052v1 Announce Type: cross Abstract: Lagrangian Relaxation (LR) is a powerful technique for solving large-scale Mixed Integer Linear Programming (MILP), particularly those with decomposab
arXiv:2510.03824v2 Announce Type: replace-cross Abstract: The task of learning a diffusion-based neural sampler for drawing samples from an unnormalized target distribution can be viewed as a stochast
arXiv:2603.07561v2 Announce Type: replace Abstract: Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often negle
arXiv:2603.01009v2 Announce Type: replace Abstract: Over the past years, Automated Essay Scoring (AES) systems have gained increasing attention as scalable and consistent solutions for assessing the p
arXiv:2605.18821v1 Announce Type: new Abstract: Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threa
arXiv:2503.22823v3 Announce Type: replace-cross Abstract: In classical information theory, the Doeblin coefficient of a classical channel provides an efficiently computable upper bound on the total-va
arXiv:2605.18775v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retri
arXiv:2603.09473v2 Announce Type: replace Abstract: Equipping robotic systems with the capacity to generate extit{ex novo} hardware during operation extends control of physical adaptability. Unlike mo
arXiv:2605.18799v1 Announce Type: cross Abstract: Large language models can fail in critic interaction not only by answering incorrectly, but also by abandoning an initially correct scientific solutio
arXiv:2511.17166v2 Announce Type: replace Abstract: Reflections of active markers in the environment are a common source of ambiguity in onboard visual relative localization. This work presents a nove
arXiv:2605.19344v1 Announce Type: new Abstract: Linguistic cues such as 'I believe' and 'probably' offer an intuitive interface for communicating confidence, yet a generalisable, principled calibratio
arXiv:2605.18766v1 Announce Type: cross Abstract: Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such a
arXiv:2605.19510v1 Announce Type: new Abstract: Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, calle
arXiv:2605.19073v1 Announce Type: cross Abstract: Representations on the Symmetric Positive Definite (SPD) manifold have garnered significant attention across different applications. In contrast, the
arXiv:2605.19230v1 Announce Type: new Abstract: Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with dise