Knowledge catalogue
arXiv:2603.01655v2 Announce Type: replace Abstract: Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of c
arXiv:2602.19174v5 Announce Type: replace Abstract: Natural language processing for the Turkic language family, spoken by over 200 million people across Eurasia, remains fragmented, with most language
arXiv:2605.23257v1 Announce Type: cross Abstract: Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Y
arXiv:2605.23274v1 Announce Type: new Abstract: Retrieving events from large-scale video datasets is challenging due to complex temporal, spatial, and multimodal information. This paper presents U-CES
arXiv:2605.23098v1 Announce Type: new Abstract: Reliable uncertainty estimation is critical for deploying monocular depth deep neural networks (DNNs) in safety-critical robotic systems. Conventional u
arXiv:2605.22968v1 Announce Type: cross Abstract: Machine learning methods provide a methodological innovation that can help screen for cardiovascular disease through noninvasive and readily available
arXiv:2605.23033v1 Announce Type: cross Abstract: Foundational Models pretrained on huge amount of data learn representations that evolve across depth, forming a hierarchy of embeddings with distinct
arXiv:2605.23171v1 Announce Type: cross Abstract: Recent advancements in instructional fine-tuning have injected noise into embeddings, with NEFTune (Jain et al., 2024) setting benchmarks using unifor
arXiv:2605.23565v1 Announce Type: cross Abstract: Reinforcement learning agents often exhibit unintended goal-directed behaviour outside their training distribution, but we currently lack a principled
arXiv:2603.18123v3 Announce Type: replace-cross Abstract: Foundation models promise to unify multiple clinical tasks within a single framework, but recent ultrasound studies report that unified models
arXiv:2605.23464v1 Announce Type: new Abstract: We consider a decentralized setup in which the participants collaboratively train and serve a large neural network, and where each participant only proc
arXiv:2507.23372v2 Announce Type: replace Abstract: Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each othe
arXiv:2503.12868v2 Announce Type: replace Abstract: Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However
arXiv:2408.03085v3 Announce Type: replace-cross Abstract: As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directl
arXiv:2510.07869v4 Announce Type: replace Abstract: Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specif
arXiv:2506.00560v2 Announce Type: replace-cross Abstract: End-to-end planning systems for autonomous driving are rapidly improving, especially in closed-loop simulation environments like CARLA. Many s
arXiv:2601.00969v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models provide strong action priors for robotic manipulation, but their reactive behavior can fail under distribu
arXiv:2605.23504v1 Announce Type: cross Abstract: Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, lab
arXiv:2605.23632v1 Announce Type: new Abstract: We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of
arXiv:2605.22851v1 Announce Type: cross Abstract: Photoplethysmography (PPG) has become a ubiquitous physiological signal; however, current generative models still struggle to preserve realistic wavef
arXiv:2605.23381v1 Announce Type: new Abstract: Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow
arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalabili
arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search
arXiv:2605.22991v1 Announce Type: new Abstract: Reactive task-space planners such as Bug2 operate with fixed Cartesian step sizes and are unaware of the manipulator's joint-angle limits. When the Jaco
arXiv:2602.07399v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations r
arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning,
arXiv:2605.22907v1 Announce Type: new Abstract: Real-world long video understanding requires models to perform continuous tracking, information integration and memory retention over massive temporal s
arXiv:2602.07801v4 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance a
arXiv:2605.23518v1 Announce Type: new Abstract: Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in m
arXiv:2605.23141v1 Announce Type: new Abstract: A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipula
arXiv:2605.23717v1 Announce Type: new Abstract: Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-
arXiv:2605.23868v1 Announce Type: new Abstract: Vision Transformers (ViTs) can learn strong image-level representations while their patch representations become less effective for dense prediction dur
arXiv:2605.22876v1 Announce Type: new Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize an
arXiv:2605.23719v1 Announce Type: cross Abstract: Vision Transformers have achieved remarkable success in computer vision, but their common use of learnable one-dimensional positional encodings weaken
arXiv:2605.23446v1 Announce Type: new Abstract: Graphs with a simple spectrum admit cubic-time isomorphism testing, yet we prove that for every natural number k, the k-Weisfeiler-Leman (k-WL) test can
arXiv:2605.23158v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) on resource-constrained devices remains challenging, spurring interest in split inference, where models
arXiv:2605.23410v1 Announce Type: cross Abstract: The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentat
arXiv:2605.23067v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Exist
arXiv:2605.22975v1 Announce Type: new Abstract: We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypotheti
arXiv:2605.23131v1 Announce Type: new Abstract: In this note, I would like to share a small research moment where Codex helped me find the right way to adapt rare switching to the private setting. The
arXiv:2605.22873v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become the default strategy for enhancing LLM capabilities, yet its application raises a fundamental question: wh
arXiv:2605.23272v1 Announce Type: cross Abstract: Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most exi
arXiv:2605.23278v1 Announce Type: new Abstract: Language models trained on observed sequences are often described as learning the conditional distribution of the next token given previous tokens. This
arXiv:2605.23540v1 Announce Type: new Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data. One key task in DR-based analysis is discovering neighborhoods
arXiv:2605.23414v1 Announce Type: new Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan
arXiv:2605.23148v1 Announce Type: new Abstract: As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs)
arXiv:2605.22995v1 Announce Type: cross Abstract: Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs) as a voca
arXiv:2605.23220v1 Announce Type: new Abstract: Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated autom
arXiv:2605.23025v1 Announce Type: new Abstract: World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured
arXiv:2605.22973v1 Announce Type: cross Abstract: Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised eva
arXiv:2511.00266v2 Announce Type: replace Abstract: Accurate trajectory prediction is crucial for safe and reliable autonomous driving systems, requiring models that capture long-term temporal depende
arXiv:2502.04230v3 Announce Type: replace-cross Abstract: The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data
arXiv:2605.23348v1 Announce Type: cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital
arXiv:2601.21198v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) architectures substantially bolster the expressive power of large-language models, their prohibitive memory foo
arXiv:2605.22341v1 Announce Type: new Abstract: Hard-label classification is usually trained with smooth surrogate losses, most prominently softmax cross-entropy. We isolate an asymptotic mechanism by
arXiv:2601.21025v3 Announce Type: replace-cross Abstract: Score-based generative models have recently achieved remarkable success. While they are usually parameterized by the score, an alternative way
arXiv:2605.22549v1 Announce Type: cross Abstract: The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension dHSIC are degenerate V-statistics whose data-dependent weighted
arXiv:2411.01332v5 Announce Type: replace Abstract: Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific dis
arXiv:2605.22622v1 Announce Type: new Abstract: Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stoc
arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conf