Knowledge catalogue
arXiv:2601.23151v2 Announce Type: replace Abstract: Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modelin
arXiv:2511.00421v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains un
arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-ba
arXiv:2609.39130v1 Announce Type: new Abstract: Accurate assessment of color differences is essential for applications ranging from digital design to quality control. While existing color difference m
arXiv:2609.39848v1 Announce Type: new Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation
arXiv:2609.38289v1 Announce Type: cross Abstract: In personalized AI applications, such as conversational assistants and recommender systems, users interact not with models in isolation but with broad
arXiv:2609.38265v1 Announce Type: cross Abstract: Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission re
arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent
arXiv:2609.39976v1 Announce Type: cross Abstract: Mobile terminals need to provide application and network services while supporting users' control over their attention. We explore voice-first interac
arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. H
arXiv:2609.38592v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) enables reconstruction without per- scene optimisation, but practical stereo-camera applications require nearb
arXiv:2609.39741v1 Announce Type: new Abstract: Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitte
arXiv:2609.36024v1 Announce Type: new Abstract: Reconstructing simulation-ready 3D scenes from real-world observations enables robotics, gaming, and immersive applications, yet existing methods largel
arXiv:2609.35814v1 Announce Type: cross Abstract: As browser-use agents improve, benchmarks keep pace by collecting new tasks, websites, and applications, often making tasks longer or more novel. This
arXiv:2609.37223v1 Announce Type: new Abstract: Credit-risk prediction is important in banking, but a prediction alone does not explain why an applicant is risky or how it should be combined with othe
arXiv:2609.37532v1 Announce Type: cross Abstract: Growing large language model applications demand efficient inference. At high concurrency, block-diffusion speculative decoding suffers from verificat
arXiv:2609.37551v1 Announce Type: cross Abstract: Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an oppo
arXiv:2609.36390v1 Announce Type: cross Abstract: Offline reinforcement learning seeks optimal decision rules from previously collected data. In some applications, a decision can be an entire function
arXiv:2607.11386v2 Announce Type: replace Abstract: UAV swarm for applications, such as indoor inspection, security patrol, and logistics delivery, are often mission-oriented rather than exploration-o
arXiv:2609.37740v1 Announce Type: new Abstract: While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid
arXiv:2609.37495v1 Announce Type: new Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exist
arXiv:2405.14749v3 Announce Type: replace-cross Abstract: Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL m
arXiv:2609.37350v1 Announce Type: new Abstract: The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising
arXiv:2609.36479v1 Announce Type: cross Abstract: Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-securit
arXiv:2609.36875v1 Announce Type: new Abstract: Accurate instance segmentation in dynamic scenes is important for downstream applications such as robotics and autonomous driving. Existing Segment Anyt
arXiv:2609.36421v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. Th
arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require te
arXiv:2609.35810v1 Announce Type: cross Abstract: Large language models are increasingly used in oncology applications, but their predictions are often weakly grounded in explicit medical structure. W
arXiv:2609.37003v1 Announce Type: new Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most ex
arXiv:2609.37287v1 Announce Type: cross Abstract: Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preservat
Written by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact
arXiv:2609.33628v1 Announce Type: new Abstract: Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt injectio
arXiv:2609.33279v1 Announce Type: new Abstract: Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements c
arXiv:2609.32628v1 Announce Type: new Abstract: Video generation has broad applications in content creation and entertainment. Diffusion transformers have advanced the quality of generated videos, but
arXiv:2609.35106v1 Announce Type: new Abstract: Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery.
arXiv:2609.34921v1 Announce Type: new Abstract: Predicting Drug-Target Interactions~(DTIs) is a central task in computational drug discovery, with direct applications in virtual screening, drug repurp
arXiv:2609.35099v1 Announce Type: new Abstract: We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU. Leveraging both optimized
arXiv:2602.04553v2 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to nuclear scatteri
Artificial intelligence is changing the makeup of internet traffic, making AI identity an emerging concern for websites and applications. They are no longer interacting only with humans and traditiona
arXiv:2609.34791v1 Announce Type: cross Abstract: We study finite-time concentration and convergence rates for projected two-time-scale stochastic approximation driven by a controlled Markov chain. Th
arXiv:2605.05660v2 Announce Type: replace Abstract: Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most exist
arXiv:2609.35527v1 Announce Type: new Abstract: Embedding structured objects into Euclidean spaces has enabled a wide range of successful machine learning applications. Such objects include words, doc
arXiv:2609.32117v1 Announce Type: new Abstract: Smartphone motion sensors support interactive applications, but their readings may also reveal touchscreen input beyond their intended use. Assuming kno
arXiv:2607.09710v2 Announce Type: replace Abstract: Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and
arXiv:2609.31988v1 Announce Type: new Abstract: We investigate the applicability of Lagrangian and Hamiltonian Neural Network models to a dissipative system that has explicit time dependence in its La
arXiv:2609.32933v1 Announce Type: new Abstract: In safety-critical applications, deployment uses a single policy, whose performance and constraint satisfaction should hold directly rather than only fo
arXiv:2609.32958v1 Announce Type: new Abstract: In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (L
arXiv:2607.17511v2 Announce Type: replace Abstract: Large Time Series Foundation Models (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application
arXiv:2609.34218v1 Announce Type: new Abstract: Looped language models separate computational depth from parameter count by repeatedly applying the same transformer block. Adapting these models requir
arXiv:2511.18038v2 Announce Type: replace-cross Abstract: Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a mult
arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit
arXiv:2609.34867v1 Announce Type: new Abstract: Vision language models have achieved strong performance across a wide range of multimodal applications, yet their substantial computational and memory c
arXiv:2609.32856v1 Announce Type: new Abstract: Vector polygon generation converts visual inputs, e.g., remote sensing (RS) images, into vectorized polygonal geometries, supporting applications such a
arXiv:2609.33420v1 Announce Type: cross Abstract: Recovering probability measures from their moments has numerous applications, esp. in connection with the method of moments in statistics and optimiza
arXiv:2609.31932v1 Announce Type: cross Abstract: Low-Earth-orbit (LEO) satellites are increasingly expected to perform onboard learning for applications such as disaster response and environmental mo
arXiv:2609.32756v1 Announce Type: new Abstract: Foundation models are rarely used as generic, frozen feature extractors; instead, they are fine-tuned for the target downstream application. This practi
arXiv:2511.15565v3 Announce Type: replace Abstract: Human pose forecasting predicts future human poses from past observations for applications including action recognition, sports analysis, and human-
arXiv:2609.34111v1 Announce Type: cross Abstract: The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive
arXiv:2609.34019v1 Announce Type: new Abstract: In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. The
arXiv:2609.33715v1 Announce Type: new Abstract: Accurate numerical solutions of partial differential equations (PDEs) are crucial in numerous science and engineering applications. In this work, we int