Knowledge catalogue
arXiv:2605.20476v1 Announce Type: new Abstract: Long-horizon video generation suffers from two intertwined issues. First, there is drift, where video quality degrades over time. Second, there are cont
arXiv:2605.21076v1 Announce Type: new Abstract: Grading German legal exam solutions faces growing volumes and a shortage of qualified graders, delaying feedback and creating a bottleneck. At the same
arXiv:2605.21247v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone of deep learning, with most existing methods rooted in graph signal processing and diffusion
arXiv:2605.20248v1 Announce Type: new Abstract: In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classificat
arXiv:2509.10308v2 Announce Type: replace Abstract: In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of progress t
arXiv:2605.20669v1 Announce Type: new Abstract: X-ray security inspection requires accurate real-time detection of prohibited items, but existing models often struggle to balance the challenges of sev
arXiv:2605.20536v1 Announce Type: new Abstract: Accurate classification of breast ultrasound images into benign, malignant, and normal categories is a critical clinical task complicated by speckle noi
arXiv:2605.20362v1 Announce Type: new Abstract: Virtual staining of histopathology images (e.g., H&E-IHC) is an emerging tool in digital pathology, enabling faster and cheaper workflows by synthesizin
arXiv:2605.21133v1 Announce Type: new Abstract: In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) S
arXiv:2605.21363v1 Announce Type: new Abstract: As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration b
arXiv:2605.20682v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot unders
arXiv:2603.22430v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without f
arXiv:2605.21347v1 Announce Type: cross Abstract: Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterat
arXiv:2605.20257v1 Announce Type: new Abstract: Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in
arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from tw
arXiv:2605.20258v1 Announce Type: new Abstract: Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given
arXiv:2603.23531v2 Announce Type: replace Abstract: Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce
arXiv:2605.20372v1 Announce Type: new Abstract: Multimodal semantic segmentation benefits remote sensing analysis by combining complementary information from different sensor modalities. In real-world
arXiv:2601.22292v2 Announce Type: replace-cross Abstract: Multi-agent social dilemmas, such as the tragedy of the commons, capture settings where individual incentives conflict with collective well-be
arXiv:2605.21258v1 Announce Type: new Abstract: Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either
arXiv:2605.20667v1 Announce Type: new Abstract: Detecting small unmanned aerial vehicles from RGB-infrared remote-sensing pairs remains challenging due to tiny target scale, cluttered backgrounds, and
arXiv:2605.20192v1 Announce Type: new Abstract: Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate
arXiv:2605.20211v1 Announce Type: new Abstract: Educational videos are a cornerstone of remote and blended learning. However, learners' fluctuating attention remains a significant barrier to effective
arXiv:2605.20436v1 Announce Type: new Abstract: Foundation models like the Segment Anything Model (SAM) demonstrate impressive zero-shot generalization but frequently degrade under diverse real-world
arXiv:2605.21446v1 Announce Type: new Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world
arXiv:2605.20523v1 Announce Type: new Abstract: Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely
arXiv:2605.20549v1 Announce Type: new Abstract: Modern vision models achieve strong performance on standard benchmarks, yet their aggregate accuracy reveals little about which scene properties drive t
arXiv:2605.20824v1 Announce Type: new Abstract: Many sequence computations are easier to study as movement through internal states than as isolated local circuits. We introduce Markovian Circuit Traci
arXiv:2603.06007v2 Announce Type: replace Abstract: Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and
arXiv:2605.21406v1 Announce Type: new Abstract: We present MC-Risk, a planner-aligned, multi-component risk field on a bird's-eye-view grid that yields early, calibrated, and class-aware risk localiza
arXiv:2605.20723v1 Announce Type: new Abstract: Deploying large deep neural networks on memory-constrained mobile devices is a central challenge in edge ML. While compression, pruning, and quantizatio
arXiv:2603.27309v2 Announce Type: replace-cross Abstract: We present MeshTailor, the first mesh-native generative framework for synthesizing edge-aligned seams on 3D surfaces. Unlike prior optimizatio
arXiv:2605.20416v1 Announce Type: new Abstract: We study whether multimodal large language models (MLLMs) can leverage crystallographic plane indices (Miller indices) as a structured latent representa
arXiv:2605.20894v1 Announce Type: new Abstract: Mobile imitation learning on portable demonstration interfaces faces two coupled bottlenecks: locomotion-contaminated action labels and inference-induce
arXiv:2605.20687v1 Announce Type: cross Abstract: Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infea
arXiv:2605.20563v1 Announce Type: cross Abstract: Recent advances in multi-agent systems have shown great potential for solving complex tasks. However, when multiple agents edit a shared codebase conc
arXiv:2605.20255v1 Announce Type: new Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not capture the heterogeneity
arXiv:2509.17931v2 Announce Type: replace Abstract: Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. Howeve
arXiv:2605.21059v1 Announce Type: new Abstract: Despite the impressive results achieved by multimodal large language models (MLLMs), their training typically relies on jointly curated multimodal data,
arXiv:2602.24138v2 Announce Type: replace Abstract: Automated recognition of surgical phases and steps is a fundamental capability for intraoperative decision support, workflow automation, and skill a
arXiv:2605.20879v1 Announce Type: new Abstract: Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain 'one-for-all' detection, but existing GGAD methods pred
arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode
arXiv:2605.20389v1 Announce Type: new Abstract: Functional MRI data exhibit high-dimensional spatiotemporal structure, making both prediction and decoding challenging. In this work, we investigate neu
arXiv:2605.21484v1 Announce Type: new Abstract: Discrete diffusion models excel at visual synthesis but rely on slow, iterative decoding. Existing single-step distillation methods attempt to bypass th
arXiv:2509.09946v2 Announce Type: replace Abstract: Multi-Target Multi-Camera Tracking (MTMC) is an essential computer vision task for automating large-scale surveillance. With camera calibration and
arXiv:2605.20539v1 Announce Type: new Abstract: The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of
arXiv:2410.15761v4 Announce Type: replace Abstract: Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answeri
arXiv:2409.04777v4 Announce Type: replace Abstract: Large Language Models have driven significant AI advancements, yet their training is resource-intensive and highly sensitive to hyper-parameter sele
arXiv:2605.21322v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without centralizing data. However, real-world deployments must simultaneously address stat
arXiv:2605.20479v1 Announce Type: new Abstract: Hyperparameter prediction is a critical practical bottleneck for model-based image denoisers, ranging from classical TV/TGV variational solvers to moder
arXiv:2605.20250v1 Announce Type: new Abstract: Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-S
arXiv:2605.21006v1 Announce Type: cross Abstract: We study the effect of different persona on extbf{sycophancy}: model's agreement with users even when the user is incorrect. The standard mitigation,
arXiv:2605.20863v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering
arXiv:2605.20289v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical, training-free route to spiking large language models. However, current pipelines primarily focus on spike-driv
arXiv:2605.21414v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-languag
arXiv:2603.23890v2 Announce Type: replace-cross Abstract: As the modern microservice architecture for cloud applications grows in popularity, cloud services are becoming increasingly complex and more
arXiv:2603.10726v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on optimizations like Automatic Prefix Caching (APC) to accelerate inference. APC works by reusing previousl
arXiv:2605.20961v1 Announce Type: new Abstract: Existing 4D-driven video diffusion models primarily target plausible generation, but faithful 4D editing requires preserving source-observed regions whi
arXiv:2605.20867v1 Announce Type: cross Abstract: Multimodal sarcasm detection requires reasoning over cross-modal incongruities between literal expression and intended meaning, yet the specific analy
arXiv:2509.24725v3 Announce Type: replace Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows comp