Knowledge catalogue
arXiv:2608.01290v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) such as Chronos have demonstrated strong forecasting capabilities across domains, yet adapting them to institution
arXiv:2608.01129v1 Announce Type: cross Abstract: Although recent robot perception research emphasizes training on data from diverse environments to improve generalization, most existing methods still
arXiv:2608.01392v1 Announce Type: new Abstract: Text-conditioned human motion generation has made rapid progress with the emergence of large-scale motion--language datasets. However, even datasets wit
arXiv:2608.00695v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent
arXiv:2608.00970v1 Announce Type: new Abstract: Object-oriented aerial vision-and-language navigation (VLN) requires searching for a described target and landing on it precisely, under long-horizon an
arXiv:2608.00613v1 Announce Type: new Abstract: Physical-world interaction is inherently dynamic, as environments can evolve during execution, requiring agents to adapt their plans under non-stationar
arXiv:2608.02100v1 Announce Type: cross Abstract: As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become
arXiv:2608.01104v1 Announce Type: new Abstract: Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Exi
arXiv:2608.00084v1 Announce Type: new Abstract: We present PixCell, a neurosymbolic system in which multimodal agents convert a visually presented photonic component into a parametric program over a s
arXiv:2603.03320v2 Announce Type: replace Abstract: Effective communication often relies on aligning a message with an audience's narrative and worldview. Narrative shift involves transforming text to
arXiv:2603.18540v2 Announce Type: replace Abstract: The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge dev
arXiv:2608.01492v1 Announce Type: new Abstract: Selecting a complete 3D object from a reconstructed scene with minimal user effort is essential for practical scene editing and embodied interaction. Ex
arXiv:2608.01686v1 Announce Type: new Abstract: In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From c
arXiv:2608.01677v1 Announce Type: new Abstract: Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techn
arXiv:2608.00776v1 Announce Type: new Abstract: Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal
arXiv:2608.02236v1 Announce Type: new Abstract: Zero-shot skeleton-based action recognition (ZSAR) aims to recognize unseen action categories by aligning skeleton features with textual semantics. Howe
arXiv:2608.01410v1 Announce Type: cross Abstract: General-purpose humanoid trackers can execute diverse references, but their zero-shot coverage depends on large embodied corpora that are costly to ex
arXiv:2603.07564v2 Announce Type: replace Abstract: Satellite video object tracking (SVOT) remains fundamentally challenging due to texture scarcity, arbitrary rotation, aspect ratio changes, and seve
arXiv:2608.00663v1 Announce Type: new Abstract: Audio-driven emotional talking face generation aims to synthesize realistic videos with expressive facial dynamics. However, existing methods struggle t
arXiv:2608.02068v1 Announce Type: new Abstract: Monocular depth foundation models, benefiting from large-scale synthetic training data, have demonstrated strong generalization. However, they often hal
arXiv:2603.23179v2 Announce Type: replace Abstract: Diffusion models provide powerful priors for 2D image completion, but these priors are learned on bounded planar images and do not transfer directly
arXiv:2608.02366v1 Announce Type: new Abstract: Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains
arXiv:2509.22910v2 Announce Type: replace Abstract: Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., pla
arXiv:2603.13418v2 Announce Type: replace Abstract: Structured pruning is widely applied to compress large language models (LLMs), but its performance depends heavily on how neuron importance is estim
arXiv:2608.01660v1 Announce Type: new Abstract: Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-to
arXiv:2608.01988v1 Announce Type: new Abstract: Rapid advances in image generation models call for interpretable AI-generated image detection methods that not only determine authenticity but also prov
arXiv:2608.02392v1 Announce Type: new Abstract: A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing
arXiv:2608.00518v1 Announce Type: new Abstract: 3D visual grounding aims to localize the target object in a 3D scene from a natural language query, requiring both fine-grained semantic understanding a
arXiv:2608.02252v1 Announce Type: new Abstract: Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the d
arXiv:2608.01918v1 Announce Type: cross Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable
arXiv:2608.01356v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tissue representations and have become central to digital pathology. However, deployment in disease-sp
arXiv:2608.00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architecture
arXiv:2608.02411v1 Announce Type: cross Abstract: Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation.
arXiv:2608.00537v1 Announce Type: new Abstract: Latent Flow Models have revolutionized compressed-space image synthesis, yet their application to high-fidelity inverse problems remains bottlenecked. I
arXiv:2608.01800v1 Announce Type: new Abstract: Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However,
arXiv:2608.01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learnin
arXiv:2608.02110v1 Announce Type: new Abstract: Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustn
arXiv:2608.00050v1 Announce Type: cross Abstract: Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variat
arXiv:2608.01737v1 Announce Type: new Abstract: As digital drawings are increasingly shared online, reliable authorship verification has become important for protecting artists and resolving disputes.
arXiv:2608.01794v1 Announce Type: cross Abstract: Universal Multimodal Embeddings (UMEs) aim to unify various modalities and tasks into a shared representation space. In recent years, this field has w
arXiv:2608.02324v1 Announce Type: new Abstract: Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heter
arXiv:2608.02437v1 Announce Type: new Abstract: Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the
arXiv:2608.00588v1 Announce Type: new Abstract: Layout-to-image diffusion models have achieved impressive semantic controllability by conditioning generation on category-level segmentation maps. Howev
arXiv:2608.02440v1 Announce Type: cross Abstract: In noisy social dilemmas, intended actions are stochastically corrupted before execution, so an observed defection may reflect hostile intent or actio
arXiv:2512.10739v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have expanded the mathematical reasoning frontier through Chain-of-Thought (CoT) techniques and Reinforcement Learning
arXiv:2608.00264v1 Announce Type: new Abstract: Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial comp
arXiv:2608.01780v1 Announce Type: new Abstract: Text-to-image (T2I) generation models are increasingly embedded in applications such as media content creation and education, raising concerns about how
arXiv:2608.02018v1 Announce Type: new Abstract: Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to in
arXiv:2608.00075v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples
arXiv:2608.01879v1 Announce Type: new Abstract: Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly
arXiv:2602.18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for
arXiv:2608.00237v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently emerged as a promising paradigm for end-to-end autonomous driving, enabling agents to map multimodal inputs
arXiv:2509.16577v2 Announce Type: replace Abstract: Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge comm
arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In
arXiv:2608.00625v1 Announce Type: new Abstract: Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control acti
arXiv:2608.01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmen
arXiv:2608.00231v1 Announce Type: new Abstract: Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only corre
arXiv:2608.00279v1 Announce Type: cross Abstract: Recent radiology-adapted vision-language models have achieved strong performance on standard report generation benchmarks, yet their robustness and ge
arXiv:2608.02432v1 Announce Type: new Abstract: Autoregressive models have emerged as an effective paradigm for point cloud generation. However, most existing approaches rely on heuristic tokenization
arXiv:2608.01672v1 Announce Type: new Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later. Test