Knowledge catalogue
arXiv:2605.19359v1 Announce Type: new Abstract: Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these ima
arXiv:2605.19080v1 Announce Type: cross Abstract: In Online Continual Learning (OCL), a neural network sequentially learns from a non-stationary data stream in a single-pass with access only to a limi
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:2605.19063v1 Announce Type: new Abstract: Inspired by long-standing open problems in algebraic combinatorics, we show that modern machine learning can meaningfully contribute to verifiable mathe
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:2605.18776v1 Announce Type: cross Abstract: The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely
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:2605.19723v1 Announce Type: cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial
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:2605.19722v1 Announce Type: cross Abstract: Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Sin
arXiv:2508.06649v3 Announce Type: replace Abstract: Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks. In this study,
arXiv:2605.19792v1 Announce Type: new Abstract: Visually-grounded language models (VLMs) are highly effective in linking visual and textual information, yet they often struggle with basic classificati
arXiv:2506.18852v2 Announce Type: replace-cross Abstract: Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying mechanisms. As the field grows in in
arXiv:2605.19027v1 Announce Type: new Abstract: Medical foundation models (MedFMs) have emerged as transformative tools in healthcare, demonstrating capabilities across diverse clinical applications.
arXiv:2605.19833v1 Announce Type: cross Abstract: Despite rapid advances in automatic speech recognition (ASR) and large audio-language models, robust recognition in real-world environments remains li
arXiv:2605.19748v1 Announce Type: new Abstract: Automatic generation of computer-aided design (CAD) models is a core technology for enabling intelligence in advanced manufacturing. Existing generation
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:2605.19307v1 Announce Type: new Abstract: Visual Question Answering (VQA), as the representative multimodal task, serves as a key benchmark for evaluating the reasoning capabilities of Multimoda
arXiv:2605.18809v1 Announce Type: cross Abstract: General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape fa
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:2605.19990v1 Announce Type: cross Abstract: Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing came
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:2510.25897v2 Announce Type: replace Abstract: The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one rew
arXiv:2605.20128v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into high-stakes decision-making. Inspired by the theory of inattentional blindness in human co
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:2605.19194v1 Announce Type: new Abstract: The Mixture-of-Agents (MoA) framework has shown promise in improving large language model (LLM) performance by aggregating outputs from multiple agents.
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:2605.19330v1 Announce Type: new Abstract: LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves, and responds. Unlike
arXiv:2605.18919v1 Announce Type: cross Abstract: Evolutionary algorithms for adversarial attacks leverage population-based search to discover perturbations without gradient information, but suffer fr
arXiv:2403.07183v4 Announce Type: replace-cross Abstract: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a lar
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:2605.18956v1 Announce Type: new Abstract: Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and
arXiv:2605.19752v1 Announce Type: new Abstract: Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in
arXiv:2605.20183v1 Announce Type: new Abstract: Video generation is rapidly evolving from single-shot synthesis to complex multi-shot audio-video (MSAV) narratives to meet real-world demands. However,
arXiv:2510.18830v2 Announce Type: replace Abstract: The adoption of long context windows has become a standard feature in Large Language Models (LLMs), as extended contexts significantly enhance their
arXiv:2605.20174v1 Announce Type: new Abstract: Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years
arXiv:2605.18870v1 Announce Type: new Abstract: In recent years, transformer architectures have revolutionized the field of language processing, opening the door to previously unforeseen possibilities
arXiv:2605.18823v1 Announce Type: new Abstract: Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios
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:2605.19701v1 Announce Type: new Abstract: The simultaneous localization and mapping community has introduced a growing number of systems adapted for multi-session operations where the operationa
arXiv:2605.18817v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising masked token sequences, offering a tradeoff between parallelism and quality comp
arXiv:2404.16676v2 Announce Type: replace-cross Abstract: We establish Multilayer Correlation Clustering, a novel generalization of Correlation Clustering to the multilayer setting. In this model, we
arXiv:2601.14822v2 Announce Type: replace-cross Abstract: Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise,
arXiv:2511.14159v2 Announce Type: replace Abstract: Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-wo
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:2503.04929v3 Announce Type: replace-cross Abstract: Planning and control for high-dimensional robot manipulators in cluttered dynamic environments require computational efficiency and robust saf
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:2605.19430v1 Announce Type: new Abstract: Flapping-Wing Micro Aerial Vehicles (FWMAVs) provide exceptional maneuverability and aerodynamic efficiency but pose significant challenges for onboard
arXiv:2605.19393v1 Announce Type: new Abstract: Deep learning models for medical image classification are susceptible to subgroup performance disparities across demographic attributes such as age, gen
arXiv:2605.20098v1 Announce Type: new Abstract: Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or
arXiv:2605.19354v1 Announce Type: cross Abstract: MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes mo
arXiv:2602.20700v2 Announce Type: replace Abstract: Estimating sewing patterns from images is a practical approach for creating high-quality 3D garments, but it remains challenging due to the scarcity
arXiv:2603.25722v2 Announce Type: replace Abstract: Contrastive vision-language (V&L) models remain a popular choice for various applications. However, several limitations have emerged, most notably t
arXiv:2510.18924v3 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) or verifiable rewards (RLVR), the standard paradigm for aligning LLMs or building recent SOT
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.