Knowledge catalogue
arXiv:2602.07104v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering di
arXiv:2608.19381v1 Announce Type: cross Abstract: Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link
arXiv:2608.19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, ex
arXiv:2608.19766v1 Announce Type: new Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and v
arXiv:2608.20134v1 Announce Type: new Abstract: We present a novel view on feature evolution in Vision Transformers (ViTs) by visualizing the training process over two dimensions -- network depth (lay
arXiv:2505.07687v4 Announce Type: replace-cross Abstract: Multi-modal medical image synthesis is pivotal for alleviating clinical data scarcity, yet existing methods fail to reconcile global anatomica
arXiv:2608.19490v1 Announce Type: cross Abstract: State-of-the-art vision-language-action (VLA) models such as pi_{0.5} exhibit strong semantic understanding, instruction following and task behavior.
arXiv:2608.19762v1 Announce Type: cross Abstract: A minibatch can influence training beyond the update at which it is observed because AdamW stores past gradient information in its optimizer states. W
arXiv:2608.19758v1 Announce Type: new Abstract: Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, partic
arXiv:2608.19659v1 Announce Type: new Abstract: Choosing tensor-parallel (TP) degrees and replica counts for an LLM serving fleet is difficult because performance is not monotonic in TP and the feasib
arXiv:2608.20112v1 Announce Type: cross Abstract: Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstructi
arXiv:2608.19965v1 Announce Type: new Abstract: Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contras
arXiv:2607.21596v2 Announce Type: replace Abstract: Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usu
arXiv:2509.08521v2 Announce Type: replace-cross Abstract: FMT^{*} plans efficiently in static worlds by expanding a cost-ordered wavefront and collision-checking lazily, but its single-pass unvisited
arXiv:2608.19611v1 Announce Type: cross Abstract: LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution of reasoning chains that it might produce for a giv
arXiv:2608.20153v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far
arXiv:2608.19680v1 Announce Type: new Abstract: Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories dem
arXiv:2608.20195v1 Announce Type: cross Abstract: Technical documentation is written for human developers, but an increasing share of software changes is now authored by autonomous coding agents. Whic
arXiv:2608.19938v1 Announce Type: cross Abstract: Existing approaches to anomalous behaviour log detection, such as Wazuh rely primarily on predefined detection rules, while statistical anomaly detect
arXiv:2606.05605v2 Announce Type: replace Abstract: How does a system that merely predicts the world come to distinguish its own causal influence from everything else? We trace this transition in a mi
arXiv:2608.19535v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieve
arXiv:2608.20026v1 Announce Type: new Abstract: Streetscape quality has become a central concern in contemporary urban planning, particularly within the framework of the pedestrian-friendly 15-minute
arXiv:2608.20331v1 Announce Type: cross Abstract: Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires both eviden
arXiv:2608.19964v1 Announce Type: new Abstract: Autonomous driving systems must operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connec
arXiv:2608.20157v1 Announce Type: new Abstract: Egocentric action understanding is often addressed using large video models pretrained on extensive exocentric datasets. However, many first-person acti
arXiv:2608.19743v1 Announce Type: new Abstract: We describe our entry to the PhysAI Dynamic 4D Reconstruction Challenge, which placed third of 27 teams at 0.58356 APD on the final leaderboard, without
arXiv:2509.13570v2 Announce Type: replace Abstract: With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important. This exploratory study examin
arXiv:2608.19549v1 Announce Type: new Abstract: This paper addresses the issue of the significant labor required to test interview dialogue systems. While interview dialogue systems are expected to be
arXiv:2604.02567v2 Announce Type: replace-cross Abstract: Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To addre
arXiv:2608.19751v1 Announce Type: new Abstract: Micro-View Order-Dispatching assigns available drivers to passenger orders within each dispatch batch and is critical to the service quality and operati
arXiv:2608.19759v1 Announce Type: cross Abstract: Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they a
arXiv:2608.19355v1 Announce Type: cross Abstract: Educational visual question answering, or VQA, requires models to solve curriculum-oriented multiple-choice questions using both language and visual e
arXiv:2608.20222v1 Announce Type: cross Abstract: The worldwide network of gravitational-wave detectors have detected more than 350 binary coalescence events till date. Future third-generation detecto
arXiv:2608.20056v1 Announce Type: new Abstract: Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets. By fusing
arXiv:2608.19994v1 Announce Type: new Abstract: We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmen
arXiv:2508.12682v2 Announce Type: replace Abstract: The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance
arXiv:2607.17417v2 Announce Type: replace Abstract: Language models are moving into chemistry and materials discovery workflows, where a wrong molecular formula, space group, or formation energy can s
arXiv:2608.20231v1 Announce Type: cross Abstract: The standard objection to full automation is demand-side: if humans earn nothing, who buys the output? This confuses an accounting role with a biologi
arXiv:2602.15648v2 Announce Type: replace-cross Abstract: Inverse design problems are common in engineering and materials science. The forward direction, i.e., computing output quantities from design
arXiv:2608.19206v1 Announce Type: cross Abstract: Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creat
arXiv:2608.20093v1 Announce Type: new Abstract: In this work, we present HandMvNet, one of the first real-time method designed to estimate 3D hand motion and shape from multi-view camera images. Unlik
arXiv:2607.17097v2 Announce Type: replace Abstract: Hand-Object Interaction (HOI) synthesis is a cornerstone for animation production and embodied AI. Despite the strong priors of video foundation mod
arXiv:2608.19430v1 Announce Type: cross Abstract: Vulnerability prioritization is inherently preference dependent, since the same CVE can receive different remediation priority under different operati
arXiv:2602.13710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment o
arXiv:2608.19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in ea
arXiv:2608.19515v1 Announce Type: new Abstract: Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves re
arXiv:2608.19377v1 Announce Type: cross Abstract: Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical syst
arXiv:2608.19407v1 Announce Type: cross Abstract: Deep Learning models can include billions of parameters or more, making it difficult to explain their internal transformations and outputs. However, e
arXiv:2608.19574v1 Announce Type: new Abstract: World action models jointly predict future visual observations and actions, whereas existing tactile-aware variants typically represent future touch as
arXiv:2608.19297v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) excel in medical applications, most of them favor static images or short-term signals. In the critical fi
arXiv:2608.19215v1 Announce Type: new Abstract: Given deep uncertainty about the possibility of artificial consciousness, it is unclear how we should treat potentially sentient AI. On the one hand, we
arXiv:2606.19161v2 Announce Type: replace Abstract: Establishing a universal benchmark for tactile representation learning in robotic manipulation remains challenging due to the diversity of tactile s
arXiv:2608.19443v1 Announce Type: new Abstract: Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robo
arXiv:2608.19395v1 Announce Type: cross Abstract: Hybrid Transformer-Mamba large language models (LLMs) enhance long-context efficiency, but their heterogeneous computation and communication patterns
arXiv:2608.20127v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces significant challenges when natural language queries must distinguish between multiple events involving visually si
arXiv:2608.19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to
arXiv:2608.19514v1 Announce Type: cross Abstract: Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this a
arXiv:2608.19857v1 Announce Type: new Abstract: For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data.
arXiv:2608.19266v1 Announce Type: cross Abstract: The OWASP Top 10 for LLM Applications ranks the risks that a community of security practitioners judges most important. We ask a narrower question: ch
arXiv:2608.20319v1 Announce Type: cross Abstract: Naturalistic computer-use traces, passively recorded screenshots and mouse or keyboard actions, are a valuable resource for deriving symbolic, auditab