Knowledge catalogue
arXiv:2609.18242v1 Announce Type: new Abstract: Force-aware Vision-Language-Action (VLA) policies improve contact-rich manipulation, but typically combine task-level motion and contact-dependent adjus
arXiv:2609.18107v1 Announce Type: new Abstract: Graph anomaly detection aims to identify graph structures (e.g., nodes, edges, or subgraphs) that deviate significantly from expected patterns, which su
arXiv:2609.17991v1 Announce Type: cross Abstract: Interactive Quantum Classifiers (IQCs) constitute a family of quantum machine learning models inspired by open quantum systems, in which the interacti
arXiv:2609.18766v1 Announce Type: cross Abstract: Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their
arXiv:2609.18293v1 Announce Type: new Abstract: Robotic data generation is a promising paradigm for scaling robot learning without collecting large-scale real-world data. However, generating geometric
arXiv:2512.22143v2 Announce Type: replace-cross Abstract: Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communicatio
arXiv:2609.18009v1 Announce Type: cross Abstract: Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accurac
arXiv:2609.18763v1 Announce Type: new Abstract: Whole-body humanoid teleoperation commonly combines a motion-tracking policy with a separate dexterous-hand retargeter. However, independently generated
arXiv:2609.18069v1 Announce Type: new Abstract: Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorptio
arXiv:2609.18358v1 Announce Type: new Abstract: Robot manipulation policies often struggle to generalize beyond their demonstrations, even when new instructions involve familiar objects and behaviors.
arXiv:2609.17547v1 Announce Type: new Abstract: AI assistants are expected to remain useful during difficult interactions, but little is known about how repeated verbal abuse changes their engagement
arXiv:2609.18431v1 Announce Type: new Abstract: More than 300 million people worldwide are affected by one of over 7,000 known rare diseases, yet diagnosis remains difficult because patients initially
arXiv:2510.25218v2 Announce Type: replace-cross Abstract: AI is changing work and decision making faster than many institutions can adapt their operating practices. We argue that this adaptation gap m
“HUMANZS LAUNCHED THE JOB” Ok, I don’t like the spelling here, but the gist of this Mozilla cybersecurity researcher rings true. The six new incidents aren’t about the second coming of Skynet, they ar
arXiv:2609.18598v1 Announce Type: cross Abstract: Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the o
arXiv:2608.27875v3 Announce Type: replace Abstract: Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the atten
arXiv:2609.19138v1 Announce Type: cross Abstract: Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstration
arXiv:2609.17777v1 Announce Type: cross Abstract: AI and large language models can recover clinically meaningful features from electronic health records (EHRs), but predictive usefulness does not esta
arXiv:2609.18034v1 Announce Type: new Abstract: Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters wit
arXiv:2609.18955v1 Announce Type: new Abstract: Efficient perception models are essential for real-time autonomous driving, where accuracy and computational cost must be carefully balanced. However, a
arXiv:2609.18490v1 Announce Type: new Abstract: 'What I cannot create, I do not understand.'Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models ha
arXiv:2609.18511v1 Announce Type: cross Abstract: In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation of
arXiv:2609.17847v1 Announce Type: new Abstract: Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in po
arXiv:2609.19040v1 Announce Type: new Abstract: Imitation learning is attractive for robot manipulation, but collecting demonstrations remains a bottleneck for multi-stage tasks requiring repeated sce
arXiv:2609.18578v1 Announce Type: new Abstract: Pathologists diagnose diseases by first locating suspicious tissue and then examining it at higher magnification, whereas self-supervised vision transfo
arXiv:2609.17638v1 Announce Type: cross Abstract: This is the set of lecture notes for the PhD course href{https://www.unibz.it/en/faculties/engineering/phd-computer-science/study-course-offering/2025
arXiv:2609.17842v1 Announce Type: cross Abstract: Conversational visual analytics (CVA) agents powered by large language models generate visualizations and natural-language explanations from open-ende
arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,
arXiv:2006.12694v2 Announce Type: replace-cross Abstract: Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely use
arXiv:2510.08928v2 Announce Type: replace Abstract: Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce
arXiv:2609.17930v1 Announce Type: new Abstract: As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether th
arXiv:2609.18445v1 Announce Type: new Abstract: Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agen
arXiv:2609.18669v1 Announce Type: new Abstract: Multi-Modal Motion Planning (M^3P) requires joint reasoning over continuous motions and discrete mode transitions, making it difficult to solve efficien
arXiv:2609.18529v1 Announce Type: new Abstract: UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is unce
arXiv:2609.18357v1 Announce Type: new Abstract: Large language model (LLM) pricing agents may respond to how market data is presented, even when its numerical values remain unchanged. We introduce mar
arXiv:2609.18088v1 Announce Type: cross Abstract: Detection-free methods for image-to-point cloud registration are prone to erroneous correspondences caused by domain and modality discrepancies, limit
arXiv:2609.18129v1 Announce Type: cross Abstract: Leaf counting is an important task in plant phenotyping for monitoring plant growth and estimating crop yield. Most existing methods rely on RGB image
arXiv:2609.17965v1 Announce Type: new Abstract: AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to stud
arXiv:2603.05071v2 Announce Type: replace Abstract: Detecting moving infrared small targets is challenging because tiny, low-contrast targets occupy few pixels and are easily obscured by dynamic backg
arXiv:2602.23994v2 Announce Type: replace-cross Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) precedes dementia. Structural MRI pro
arXiv:2609.17964v1 Announce Type: cross Abstract: This paper extends Mixed-Integer Differentiable Predictive Control (MI-DPC) to multi-modal discrete decisions and nonconvex polynomial dynamics arisin
arXiv:2609.17763v1 Announce Type: new Abstract: Next-day wildfire prediction requires models whose forecasts can be evaluated alongside the assumptions and historical evidence used in their computatio
arXiv:2609.17987v1 Announce Type: new Abstract: The traditional Batak Ulos weaving industry faces growing challenges in producing diverse, innovative motifs due to limitations in conventional, manuall
arXiv:2609.18441v1 Announce Type: cross Abstract: Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of
arXiv:2609.18891v1 Announce Type: cross Abstract: Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedsi
arXiv:2609.17825v1 Announce Type: new Abstract: Understanding how deep neural networks make decisions remains a fundamental challenge. We present NObSP (Nonlinear Oblique Subspace Projections), a fram
arXiv:2609.18898v1 Announce Type: new Abstract: Training-free weighted aggregation is widely used to lift 2D semantic features onto 3D Gaussians for open-vocabulary scene understanding, yet its theore
arXiv:2609.18191v1 Announce Type: new Abstract: Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reli
arXiv:2609.17709v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differe
arXiv:2609.18117v1 Announce Type: new Abstract: Dexterous grasp synthesis has advanced rapidly in generating stable and physically plausible hand poses, but real-world manipulation requires grasps tha
arXiv:2609.18605v1 Announce Type: cross Abstract: As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and fin
arXiv:2609.18732v1 Announce Type: new Abstract: Humanoid robots can step over, squeeze past, and duck under obstacles, but learning to select and coordinate these behaviors from onboard perception rem
arXiv:2609.18329v2 Announce Type: new Abstract: Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation p
arXiv:2609.18588v1 Announce Type: new Abstract: For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existi
arXiv:2505.03590v2 Announce Type: replace-cross Abstract: Magnetic resonance spectroscopy (MRS) is a non-invasive technique to measure the metabolic composition of tissues, offering valuable insights
arXiv:2609.18125v1 Announce Type: cross Abstract: Humans often observe others before interacting and adjust their behavior accordingly. Robot navigation in crowds, however, often represents pedestrian
arXiv:2609.19077v1 Announce Type: cross Abstract: Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human c
arXiv:2603.27526v2 Announce Type: replace Abstract: Protein fitness optimization is a discrete search problem, and the representation used for prediction also determines the neighborhood graph travers
arXiv:2609.18752v1 Announce Type: new Abstract: Open-vocabulary object retrieval remains challenging in complex underwater environments. Although underwater image enhancement (UIE) can improve visual
arXiv:2609.17995v1 Announce Type: new Abstract: Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the un