Knowledge catalogue
arXiv:2604.08301v1 Announce Type: new Abstract: The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently,
arXiv:2604.07747v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) can improve low-k reasoning accuracy while narrowing solution coverage on challenging math que
arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob
arXiv:2610.17824v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs). It rest
arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo
arXiv:2610.12127v1 Announce Type: cross Abstract: Interactive scene exploration requires frequent view updates, although small camera motions preserve much of the visible content. Conventional 3D Gaus
arXiv:2610.09185v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electroca
arXiv:2610.09397v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame ac
arXiv:2610.10088v1 Announce Type: cross Abstract: Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferabl
arXiv:2610.07640v1 Announce Type: new Abstract: State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These agents do not
arXiv:2610.05298v1 Announce Type: new Abstract: Motion-deblurring datasets are commonly synthesised by averaging N consecutive high-frame-rate frames and labelling the result with the central frame. W
arXiv:2610.03252v1 Announce Type: new Abstract: Generative models of human-object interaction are bounded by the data that exists: everyday activities involve several objects, but most captured datase
arXiv:2610.03273v1 Announce Type: new Abstract: Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concept
arXiv:2610.03234v1 Announce Type: cross Abstract: We present WAMpy, a Python framework optimized for synthesizing Prolog programs. Unlike general-purpose Prolog systems, WAMpy targets workloads that r
arXiv:2610.00063v1 Announce Type: cross Abstract: Instance-billed serverless platforms charge for CPU and memory over the lifetime of a warm instance, making idle inference state a direct serving cost
arXiv:2609.38923v1 Announce Type: new Abstract: Working agents need to read diverse files, coordinate tools, and produce deliverables. Training such agents requires tasks built on many real files with
arXiv:2609.33611v1 Announce Type: new Abstract: Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents rai
arXiv:2609.35499v1 Announce Type: new Abstract: As the world moves towards sustainable energy sources, hydrogen (H2) can be treated as an eco-friendly alternative to fossil fuels due to its high energ
arXiv:2609.35120v1 Announce Type: new Abstract: Low-cost handheld ultrasound devices can be widely deployed compared to professional hospital ultrasound machines. However, their images suffer from com
arXiv:2609.33066v1 Announce Type: new Abstract: Contemporary neural inference architectures rely on dense floating-point weight matrices stored in high-bandwidth memory (VRAM), incurring severe memory
arXiv:2609.30975v1 Announce Type: cross Abstract: Speech content representations are central to voice conversion, speech-to-speech translation, and multimodal language models, yet they are rarely comp
arXiv:2609.30234v1 Announce Type: new Abstract: Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generati
arXiv:2609.30115v1 Announce Type: cross Abstract: Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and
arXiv:2609.26793v1 Announce Type: new Abstract: Compositional 3D scene reconstruction has recently been explored from two directions: agentic reasoning that provides semantic understanding of spatial
arXiv:2609.25411v1 Announce Type: cross Abstract: Classifier-free Guidance (CFG) is widely adopted in text-to-speech (TTS) systems to enhance generation quality and conditioning fidelity by interpolat
arXiv:2609.24310v1 Announce Type: cross Abstract: Text-to-speech models for Bantu tonal languages are challenged by a tonal system that is rooted in both the lexis (i.e., the inventory of words, stems
arXiv:2609.21334v1 Announce Type: cross Abstract: Effective evaluation of zero-day jamming detectors requires robust adversarial models. However, existing attack models often assume prior knowledge of
arXiv:2609.21158v1 Announce Type: new Abstract: We explore the use of hybrid quantum-classical generative adversarial networks for synthesising CAD-ready vector geometries. Unlike prior work that oper
arXiv:2609.17249v1 Announce Type: new Abstract: Finite-dimensional Koopman models enable efficient linear prediction and control of nonlinear robotic systems. However, models learned purely from traje
arXiv:2609.16059v1 Announce Type: new Abstract: Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fine-T
arXiv:2609.13909v1 Announce Type: cross Abstract: Continuous-latent Autoregressive Diffusion Transformer (AR-DiT) models have demonstrated immense potential in zero-shot speech generation. However, th
arXiv:2510.26046v3 Announce Type: replace-cross Abstract: Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic
I have created a VAE model using PyTorch on White Wine dataset. Basically, the main goal is to discover a brand-new white wine recipe. It puts all the wines into a latent space, finds the best part wh
arXiv:2609.07939v1 Announce Type: new Abstract: We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets,
arXiv:1706.04580v3 Announce Type: replace Abstract: The design and organization of complex robotic systems traditionally requires laborious trial-and-error processes to ensure both hardware and softwa
arXiv:2609.04174v1 Announce Type: new Abstract: 3D Foundation Models (3DFMs) such as VGGT have recently pushed the boundaries of 3D vision by predicting rich unified representations with feed-foward t
arXiv:2609.02683v1 Announce Type: new Abstract: Earth observation is fundamentally multi-scale; geospatial tasks span varied resolutions, and satellite imagery is organized into cascading tile pyramid
arXiv:2609.01032v1 Announce Type: cross Abstract: Automated mining of formal specifications is vital for verifying real-time systems. However, existing passive learning approaches remain restricted to
arXiv:2608.30550v1 Announce Type: new Abstract: Multimodal Sewing pattern generation aims to infer executable sewing patterns from design cues such as sketches and textual descriptions. As an interpre
arXiv:2608.26947v1 Announce Type: cross Abstract: Embodied agents need environments that are visually diverse, physically interactive, and changing over time. Procedural simulators can generate large
arXiv:2608.25657v1 Announce Type: cross Abstract: Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training dat
arXiv:2608.21481v1 Announce Type: cross Abstract: We participated in the BIC-MAC Challenge with a multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using
arXiv:2608.23175v1 Announce Type: new Abstract: In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth
arXiv:2608.20823v1 Announce Type: new Abstract: A humanoid has infinitely many ways to stand up from sitting while maintaining balance, making sit-to-stand (STS) a challenging control problem. We synt
arXiv:2608.20387v1 Announce Type: cross Abstract: While recent text-to-speech (TTS) models achieve high naturalness, controlling fine-grained expression via natural-language instructions remains chall
arXiv:2601.18305v2 Announce Type: replace Abstract: Despite numerous Graphical User Interface (GUI) agents claiming to automate user interaction tasks, to date, few achieve satisfactory interaction ca
arXiv:2608.18709v1 Announce Type: cross Abstract: Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain generalizati
arXiv:2608.19055v1 Announce Type: new Abstract: Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing real
arXiv:2603.24126v2 Announce Type: replace Abstract: When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likeli
arXiv:2603.23924v2 Announce Type: replace Abstract: Text-to-image models often struggle to synthesize correct occlusion relationships among multiple objects, especially in densely overlapping regions.
arXiv:2608.15303v1 Announce Type: new Abstract: Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly u
arXiv:2608.14700v1 Announce Type: new Abstract: Precise emotion control in audio-driven talking heads remains a challenge due to the reliance on implicit emotion regulation in existing systems, which
arXiv:2602.13157v2 Announce Type: replace-cross Abstract: Presented is an algorithm to synthesize the optimal infinite-horizon LQR feedback controller for continuous-time systems. The algorithm does n
arXiv:2608.13741v1 Announce Type: new Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation. However, existing text-co
arXiv:2608.14306v1 Announce Type: new Abstract: This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evi
arXiv:2405.17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. E
arXiv:2608.10765v1 Announce Type: new Abstract: Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierar
arXiv:2501.11655v3 Announce Type: replace-cross Abstract: This paper proposes a novel learning approach for designing Kazantzis-Kravaris or nonlinear Luenberger (KKL) observers for autonomous nonlinea
arXiv:2608.10941v1 Announce Type: new Abstract: Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operationa
arXiv:2608.10519v1 Announce Type: new Abstract: InfinityStar extends visual autoregressive generation to video through a sequence of image and clip pyramids. Its changing scale and cross-clip context,