Knowledge catalogue
arXiv:2608.00577v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have become a dominant architecture for large-scale AI services, yet deploying them over geo-distributed heterogeneous
arXiv:2604.00086v2 Announce Type: replace-cross Abstract: The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks.
arXiv:2608.01358v1 Announce Type: new Abstract: Search-augmented large language model agents are increasingly capable of solving knowledge-intensive tasks, but their behavior when a multi-hop question
arXiv:2606.14741v2 Announce Type: replace Abstract: We introduce HorusEye, Language as Dynamic Attention for Emergency Visual Analysis. Our investigation followed five stages. The first one is benchma
When people think about digital transformation in banking, they often focus on the visible results: mobile apps and new digital services. But there's an invisible infrastructure making all these servi
arXiv:2608.00284v1 Announce Type: new Abstract: This paper presents an applied case study on hybrid visual attention estimation for human-robot interaction using an expressive robotic head based on th
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.
If you missed today's K3 on Fireworks webinar, we've got you covered. We'll be partnering with @arizeai to chat more models on Thursday, August 6th. Sign up below! Next session, August 6 we'll be co l
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:2608.00490v1 Announce Type: new Abstract: Can image-editing models discover visual rules in image space and complete problem-solving end-to-end? We tackle this question in the spirit of a human
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:2606.13485v3 Announce Type: replace-cross Abstract: Safe rehabilitation is an interaction-dynamics problem: the controller must regulate a prescribed motion while absorbing involuntary spasm, vo
arXiv:2608.01157v1 Announce Type: new Abstract: Large language models have made text the default medium for human--AI interaction, buttext alone cannot express the full range of responses required by
arXiv:2608.01180v1 Announce Type: new Abstract: Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and
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:2607.15732v2 Announce Type: replace Abstract: Visual grounding with multimodal large language models is commonly formulated as autoregressive coordinate generation, where a model outputs boundin
arXiv:2608.00569v1 Announce Type: new Abstract: Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GP
arXiv:2608.01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy
arXiv:2608.02257v1 Announce Type: new Abstract: Mobile manipulation is a key capability for embodied intelligence, enabling robots to accomplish complex multi-stage tasks in open-world environments. H
arXiv:2608.00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can dif
arXiv:2608.00464v1 Announce Type: new Abstract: Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robo
arXiv:2608.01094v1 Announce Type: new Abstract: Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', t
arXiv:2608.01614v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of So
arXiv:2608.01771v1 Announce Type: new Abstract: We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control. Achieving this
arXiv:2608.02515v1 Announce Type: new Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retri
arXiv:2510.27313v3 Announce Type: replace-cross Abstract: Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally
arXiv:2608.01734v1 Announce Type: new Abstract: Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cel
arXiv:2510.20449v2 Announce Type: replace Abstract: Instruction tuning is crucial for aligning Large Language Models (LLMs), yet the quality of instruction-following data varies significantly. While h
arXiv:2603.03672v2 Announce Type: replace Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Exis
arXiv:2608.00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue. While classical methods typically foc
arXiv:2608.02404v1 Announce Type: new Abstract: The Loggia dei Lanzi in the Piazza della Signoria is one of Florence's most prominent structures visited by millions every year. Its construction histor
arXiv:2608.01074v1 Announce Type: cross Abstract: Tabular data is used extensively in many real-world use cases. Deep learning models have been developed to deal with tabular data, but generally perfo
arXiv:2608.00096v1 Announce Type: new Abstract: Current deep learning-based character vision studies, e.g., text recognition, character image denoising, and historical text completion, are offering ne
arXiv:2608.00925v1 Announce Type: new Abstract: Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale
arXiv:2608.02346v1 Announce Type: new Abstract: Old photographs often suffer from multiple coupled degradations, including scratches, cracks, fading, blur, noise, and missing regions, severely degradi
arXiv:2603.19258v2 Announce Type: replace Abstract: Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the
arXiv:2608.00736v1 Announce Type: new Abstract: Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, st
arXiv:2608.00195v1 Announce Type: cross Abstract: High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often f
arXiv:2608.00854v1 Announce Type: new Abstract: Microrobots hold significant potential for various applications, where targeted navigation is a basic requirement. Deep reinforcement learning (DRL) has
arXiv:2608.00400v1 Announce Type: new Abstract: Nonlocal partial differential equations arise in many applications but are often difficult to model and learn because of the presence of nonlocal operat
arXiv:2608.02449v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) for safety-critical spatial reasoning on resource-constrained autonomous driving platforms requires both compact
arXiv:2608.01153v1 Announce Type: new Abstract: Statistical subword tokenizers can process arbitrary text, but their units need not align with lexical or grammatical structure. This is especially impo
arXiv:2608.01628v1 Announce Type: new Abstract: Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural corr
arXiv:2608.01775v1 Announce Type: new Abstract: Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring
arXiv:2608.00533v1 Announce Type: new Abstract: Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual
arXiv:2608.00059v1 Announce Type: new Abstract: The success of connectome mapping now shifts the challenge of understanding the nervous system to the interpretation of neural circuits. Here, we devise
arXiv:2407.20432v3 Announce Type: replace Abstract: Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some s
arXiv:2608.00752v1 Announce Type: new Abstract: Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudina
arXiv:2608.00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on
arXiv:2602.13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty. Minimizin
arXiv:2608.00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physica
arXiv:2608.00713v1 Announce Type: new Abstract: This paper describes Observatorio Lazaro, a language resource that monitors unassimilated lexical borrowings (predominantly English lexical borrowings o
arXiv:2608.01041v1 Announce Type: cross Abstract: Machine-learning predictors estimate processor performance far faster than cycle-level simulation. For design-space exploration, however, the valuable
arXiv:2607.18295v2 Announce Type: replace Abstract: We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability
arXiv:2407.15260v3 Announce Type: replace Abstract: Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical
arXiv:2409.03901v4 Announce Type: replace Abstract: Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and r
arXiv:2608.01060v1 Announce Type: new Abstract: Cross-view geo-localization (CVGL) retrieves geo-tagged satellite imagery for a ground-view query. Most systems exhaustively search a flat, fixed-resolu
arXiv:2406.14657v4 Announce Type: replace Abstract: We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate communit