Knowledge catalogue
arXiv:2604.13835v1 Announce Type: new Abstract: Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) h
arXiv:2604.13513v1 Announce Type: new Abstract: Cardiovascular diseases account for around 17.9 million deaths per year globally, the treatment of which is challenging considering the confined space a
arXiv:2604.13991v1 Announce Type: new Abstract: Large language models (LLMs) are prone to generating factually incorrect outputs. Recent work has applied conformal prediction to provide uncertainty es
arXiv:2604.13304v1 Announce Type: new Abstract: Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoenc
arXiv:2604.13589v1 Announce Type: new Abstract: We present Dehaze-then-Splat, a two-stage pipeline for multi-view smoke removal and novel view synthesis developed for Track~2 of the NTIRE 2026 3D Rest
arXiv:2604.13509v1 Announce Type: new Abstract: Recent advances in video generation models has significantly accelerated video generation and related downstream tasks. Among these, video stylization h
arXiv:2509.25549v2 Announce Type: replace Abstract: Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical to impro
arXiv:2512.17654v3 Announce Type: replace Abstract: We present three variants of a lightweight, fully connected artificial neural network, suited for interactive estimation of three-dimensional, spati
arXiv:2512.10605v2 Announce Type: replace Abstract: We propose LEO-RobotAgent, a general-purpose language-driven intelligent agent framework for robots. Under this framework, LLMs can operate differen
arXiv:2604.13432v1 Announce Type: new Abstract: Token compression is crucial for mitigating the quadratic complexity of self-attention mechanisms in Vision Transformers (ViTs), which often involve num
arXiv:2510.05786v3 Announce Type: replace-cross Abstract: Mobius inversion and Shapley values are two mathematical tools for characterizing and decomposing higher-order structure in complex systems. T
arXiv:2510.07019v3 Announce Type: replace Abstract: Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recal
arXiv:2507.14005v2 Announce Type: replace Abstract: It was recently shown that dynamic programming (DP) methods for finding static CVaR-optimal policies in Markov Decision Processes (MDPs) can fail wh
arXiv:2604.04101v2 Announce Type: replace Abstract: This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challen
arXiv:2506.09163v2 Announce Type: replace Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic process
Auto-generated synthesis of 1623 entries about arxiv-cs-ai
Auto-generated synthesis of 663 entries about arxiv-cs-lg
arXiv:2604.12196v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate multiple candidate responses for a given prompt, yet selecting the most reliable one remains challengin
arXiv:2604.12210v1 Announce Type: new Abstract: Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely
arXiv:2604.11915v1 Announce Type: cross Abstract: Modern machine learning methods have been proposed to detect life in extraterrestrial samples, drawing on their ability to distinguish biotic from abi
arXiv:2602.15355v3 Announce Type: replace Abstract: The emergence of 3D Gaussian Splatting has fundamentally redefined the capabilities of photorealistic neural rendering by enabling high-throughput s
arXiv:2604.12799v1 Announce Type: cross Abstract: The rise of automated bidding strategies in online advertising presents new challenges in designing and analyzing efficient auction mechanisms. In thi
arXiv:2602.04850v2 Announce Type: replace-cross Abstract: Quantum chemistry is a foundational enabling tool for the fields of chemistry, materials science, computational biology and others. Despite of
arXiv:2503.10676v2 Announce Type: replace-cross Abstract: We study the efficacy of fine-tuning Large Language Models (LLMs) for the specific task of report (government archives, news, intelligence rep
Cursor, the AI-powered code editor, implemented Vercel's Microfrontends and Feature Flags infrastructure to build a rapid growth iteration loop that enables independent team deployments and controlled
arXiv:2604.12709v1 Announce Type: cross Abstract: Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with sign
arXiv:2604.12591v1 Announce Type: new Abstract: Compensatory trunk movements (CTMs) are commonly observed after stroke and can lead to maladaptive movement patterns, limiting targeted training of affe
Today, we’re announcing U.S. and EU multi-region endpoints for Claude on Vertex AI are available in public preview. By pooling capacity across multiple regions, these endpoints dynamically route reque
arXiv:2604.12041v1 Announce Type: cross Abstract: This work is concerned with the continuum limit of a graph-based data visualization technique called the t-Distributed Stochastic Neighbor Embedding (
arXiv:2603.23488v2 Announce Type: replace Abstract: Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training data scale and diversity. We argue it is
arXiv:2604.12995v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability t
arXiv:2604.11332v1 Announce Type: cross Abstract: Deep learning has markedly advanced image based plant disease diagnosis as improved hardware and dataset quality have enabled increasingly accurate ne
arXiv:2604.10116v1 Announce Type: cross Abstract: Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a s
arXiv:2604.09979v1 Announce Type: cross Abstract: Self-supervised representation learning is central to modern machine learning because it extracts structured latent features from unlabeled data and e
arXiv:2508.03793v2 Announce Type: replace Abstract: Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, includi
arXiv:2604.10173v1 Announce Type: cross Abstract: Magnetic point-group analysis classifies altermagnets but returns only a binary symmetry verdict, leaving spin-splitting energy (SSE) inaccessible wit
arXiv:2604.10963v1 Announce Type: new Abstract: Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medic
arXiv:2604.10882v1 Announce Type: cross Abstract: Graph Neural Network pretraining is pivotal for leveraging unlabeled graph data. However, generalizing across heterogeneous domains remains a major ch
arXiv:2502.18026v3 Announce Type: replace-cross Abstract: Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging t
arXiv:2604.10560v1 Announce Type: new Abstract: Profiled Sparse Networks (PSN) replace uniform connectivity with deterministic, heterogeneous fan-in profiles defined by continuous, nonlinear functions
arXiv:2604.10164v1 Announce Type: new Abstract: Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at ca
arXiv:2604.10643v1 Announce Type: new Abstract: Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is
arXiv:2604.11178v1 Announce Type: cross Abstract: Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neuro
arXiv:2604.10908v1 Announce Type: new Abstract: Every existing knowledge system separates storage from computation. We show this separation is unnecessary and eliminate it. In a standard triple is_a(A
arXiv:2604.11109v1 Announce Type: cross Abstract: As high-performance computing and AI workloads become increasingly dependent on GPUs, maintaining high performance across rapidly evolving hardware ge
arXiv:2508.11290v3 Announce Type: replace Abstract: LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful
arXiv:2604.11200v1 Announce Type: cross Abstract: Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream bu
arXiv:2604.11253v1 Announce Type: cross Abstract: Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable impo
arXiv:2604.11685v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become a state-of-the-art framework for real-time, high-fidelity novel view synthesis. However, its substantial storage
arXiv:2604.09649v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have opened new platforms for human-computer interaction, medical diagnostics, and neurorehabilitation. Wearable BCI
arXiv:2412.15803v2 Announce Type: replace-cross Abstract: Advancements in large language models (LLMs) have unlocked remarkable capabilities. While deploying these models typically requires server-gra
arXiv:2602.12748v3 Announce Type: replace Abstract: The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: In
arXiv:2604.08749v1 Announce Type: new Abstract: How many of a neural network's parameters actually encode task-specific information? We investigate this question with LottaLoRA, a training paradigm in
arXiv:2604.09437v1 Announce Type: new Abstract: A novel regularization technique, AdaCubic, is proposed that adapts the weight of the cubic term. The heart of AdaCubic is an auxiliary optimization pro
arXiv:2604.08847v1 Announce Type: new Abstract: Deepfake detection has become a fundamental component of modern media forensics. Despite significant progress in detection accuracy, most existing metho
arXiv:2511.06756v3 Announce Type: replace Abstract: Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to b
arXiv:2604.08644v1 Announce Type: new Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integr
arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot beha
arXiv:2604.09075v1 Announce Type: new Abstract: Large language models increasingly operate under multiple instructions from heterogeneous sources with different authority levels, including system poli
arXiv:2604.09263v1 Announce Type: cross Abstract: We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regre