Knowledge catalogue
arXiv:2604.24103v1 Announce Type: new Abstract: Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from
arXiv:2604.24012v1 Announce Type: new Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algo
arXiv:2604.24397v1 Announce Type: cross Abstract: In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error m
arXiv:2604.23743v1 Announce Type: cross Abstract: Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systema
arXiv:2604.24013v1 Announce Type: cross Abstract: The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, T
arXiv:2512.10570v2 Announce Type: replace-cross Abstract: We propose a flexible deep neural network (DNN) framework for modeling survival data within a partially linear regression structure. The appro
arXiv:2604.23658v1 Announce Type: cross Abstract: Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, cur
arXiv:2604.23839v1 Announce Type: cross Abstract: Measurement-critical ultrasound tasks often depend on a small anatomical region, making global reconstruction metrics an unreliable proxy for clinical
arXiv:2604.23197v1 Announce Type: new Abstract: Delayed feedback poses a core challenge for online CVR prediction, forcing a trade-off between label accuracy and data freshness. Existing methods addre
arXiv:2604.24391v1 Announce Type: new Abstract: Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a prom
arXiv:2604.23452v1 Announce Type: new Abstract: Vision Transformers trained only on image classification routinely transfer to tasks that demand spatial understanding, yet they receive no spatial supe
arXiv:2604.23776v1 Announce Type: cross Abstract: Accurate monitoring of oil palm plantations is critical for balancing economic development with environmental conservation in Southeast Asia. However,
arXiv:2604.23635v1 Announce Type: cross Abstract: Domestic voice assistants and smart-home devices are increasingly embedded in everyday routines, yet their ethics are often treated as an afterthought
arXiv:2604.23277v1 Announce Type: cross Abstract: Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context co
arXiv:2604.22839v1 Announce Type: cross Abstract: Precise Event Spotting (PES) is essential in fast-paced sports such as tennis, where fine-grained events occur within very short temporal windows. Acc
arXiv:2604.05621v2 Announce Type: replace Abstract: We present FunRec, a method for reconstructing functional 3D digital twins of indoor scenes directly from egocentric RGB-D interaction videos. Unlik
arXiv:2512.09373v2 Announce Type: replace Abstract: Registration of multiview point clouds conventionally relies on extensive pairwise matching to build a pose graph for global synchronization, which
arXiv:2511.22125v2 Announce Type: replace Abstract: Visual and textual soft prompt tuning can effectively improve the adaptability of Vision-Language Models (VLMs) in downstream tasks. However, fine-t
arXiv:2602.15846v2 Announce Type: replace Abstract: Decoder-only large language models achieve strong broad performance but are brittle to minor grammatical perturbations, undermining reliability for
arXiv:2604.23010v1 Announce Type: new Abstract: High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of auto
arXiv:2512.20220v2 Announce Type: replace Abstract: We study offline multitask reinforcement learning in settings where multiple tasks share a low-rank representation of their action-value functions.
arXiv:2604.22894v1 Announce Type: cross Abstract: Computed tomography (CT)-based attenuation and scatter correction improves quantitative PET but adds radiation exposure that is particularly undesirab
arXiv:2604.24188v1 Announce Type: new Abstract: Accurately estimating friction coefficients between arbitrary material pairs is critical for robotics, digital fabrication, and physics-based simulation
arXiv:2604.24322v1 Announce Type: new Abstract: The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion syst
arXiv:2604.23904v1 Announce Type: cross Abstract: Synthetic data offers a promising tool for privacy-preserving data release, augmentation, and simulation, but its use in causal inference requires pre
arXiv:2604.07105v2 Announce Type: replace Abstract: We present Genie Sim PanoRecon, a feed-forward Gaussian-splatting pipeline that delivers high-fidelity, low-cost 3D scenes for robotic manipulation
arXiv:2510.07324v2 Announce Type: replace-cross Abstract: We derive a general system of ODEs and associated explicit solutions in a special case for geodesics between full rank matrices in the deep li
arXiv:2604.24238v1 Announce Type: new Abstract: Diffusion models are a leading paradigm for data generation, but training-free editing typically re-runs the full denoising trajectory for every edit st
arXiv:2604.24469v1 Announce Type: cross Abstract: Content-based image retrieval (CBIR) systems enable users to search images based on visual content instead of relying on metadata. The text domain has
arXiv:2604.23651v1 Announce Type: new Abstract: Robust in-bed human pose estimation under blanket occlusion remains challenging due to the scarcity of reliable labeled training data for heavily covere
arXiv:2604.23888v1 Announce Type: cross Abstract: Adaptation of blackbox generative models has been widely studied recently through the exploration of several methods including generator fine-tuning,
arXiv:2503.10666v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become widely used across various domains spanning search engines, code generation, and text creation. Howev
arXiv:2604.24062v1 Announce Type: new Abstract: Extracting abstract causal structures and applying them to novel situations is a hallmark of human intelligence. While Large Language Models (LLMs) and
arXiv:2604.23675v1 Announce Type: cross Abstract: This work presents GS-DOT, a novel image reconstruction framework based on Gaussian Splatting (GS) for diffuse optical tomography (DOT). Inspired by G
arXiv:2601.19906v2 Announce Type: replace-cross Abstract: Targeting error-tolerant applications, approximate computing relaxes rigid functional equivalence to significantly improve power, performance,
arXiv:2604.24487v1 Announce Type: new Abstract: Guiding Vector Fields (GVFs) are a powerful tool for robotic path following. However, classical methods assume smooth, ordered curves and fail when path
arXiv:2501.07237v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing benchmarks. However, the es
arXiv:2604.23335v1 Announce Type: new Abstract: Knee osteoarthritis (KOA) is a degenerative joint disease that can lead to chronic pain, reduced mobility, and long-term disability. Automated severity
arXiv:2604.22990v1 Announce Type: cross Abstract: Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, m
arXiv:2511.08425v3 Announce Type: replace Abstract: Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distri
arXiv:2604.23547v1 Announce Type: cross Abstract: Efficient hardware implementation of nonlinear activation functions is a crucial task in deploying artificial neural networks on resource-constrained
arXiv:2604.23647v1 Announce Type: cross Abstract: In Transformer models, non-GEMM (non-General Matrix Multiplication) operations -- especially Softmax and Layer Normalization (LayerNorm) -- often domi
arXiv:2604.23717v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) demonstrate remarkable capabilities in processing extended multi-modal sequences, yet incur high inference
arXiv:2512.16378v4 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which directly proce
arXiv:2604.24558v1 Announce Type: new Abstract: Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option r
arXiv:2602.01338v2 Announce Type: replace Abstract: We present algorithms for diffusion model sampling which obtain elta-error in polylog(1/elta) steps, given access to widetilde O(elta)-accurate scor
arXiv:2604.23573v1 Announce Type: cross Abstract: Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We addre
arXiv:2604.24606v1 Announce Type: new Abstract: Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi-body nature of the syste
i just spent $10,000 on AI credits building out my project for @AIPSummit and i can't be thankful enough to @NousResearch's Hermes harness and it's self-learning loops which made this possible thank y
arXiv:2604.24196v1 Announce Type: cross Abstract: This paper analyzes identifiability and stability for the drifting field underlying distributional matching in the Generative Drifting framework of De
arXiv:2604.23584v1 Announce Type: new Abstract: Multi-modal retrieval-augmented generation (MRAG) systems retrieve visual evidence from large image corpora to ground the responses of large multi-modal
arXiv:2604.23732v1 Announce Type: cross Abstract: Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored mo
arXiv:2604.22764v1 Announce Type: cross Abstract: Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking.
arXiv:2402.15995v2 Announce Type: replace-cross Abstract: We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly litt
arXiv:2604.24040v1 Announce Type: cross Abstract: Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization ev
arXiv:2503.04338v2 Announce Type: replace-cross Abstract: Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), imp
arXiv:2603.15130v2 Announce Type: replace Abstract: Indirectness is a common feature of daily communication, yet is underexplored in NLP research for both low-resource as well as high-resource languag
arXiv:2604.23436v1 Announce Type: cross Abstract: Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable effici
arXiv:2604.24616v1 Announce Type: new Abstract: In this paper, we report the world's first infrastructure-guided communication-enhanced road crack detection pipeline that is effective and implementabl
arXiv:2604.24276v1 Announce Type: new Abstract: Instance-sensitive losses for semantic segmentation such as blob loss and CC loss were designed to address instance imbalance, ensuring small lesions ge