Knowledge catalogue
arXiv:2505.14234v2 Announce Type: replace-cross Abstract: Entropic measures like Shannon entropy (SE), its quantum mechanical analogue von Neumann entropy, and Kullback-Leibler divergence (KL) are key
arXiv:2604.22534v1 Announce Type: cross Abstract: Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and struc
arXiv:2604.22334v1 Announce Type: new Abstract: Recent advances in pretraining 3D point cloud encoders (e.g., Point-BERT, Point-MAE) have produced powerful models, whose abilities are typically evalua
arXiv:2604.22176v1 Announce Type: cross Abstract: Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerabil
arXiv:2604.22190v1 Announce Type: cross Abstract: CLIP-based person re-identification (ReID) methods aggregate spatial features into a single global exttt{[CLS]} token optimized for image-text alignme
arXiv:2604.22601v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise in automated software engineering, yet their guarantee of correctness is frequently undermined by erroneous
arXiv:2602.21750v2 Announce Type: replace Abstract: Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de
arXiv:2604.22615v1 Announce Type: new Abstract: Embodied foundation models have achieved significant breakthroughs in robotic manipulation, yet they still depend heavily on large-scale robot demonstra
arXiv:2604.22045v1 Announce Type: cross Abstract: Feature attribution methods explain the predictions of deep neural networks by assigning importance scores to individual input features. However, most
arXiv:2601.10386v2 Announce Type: replace-cross Abstract: Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Mu
arXiv:2604.22193v1 Announce Type: new Abstract: Large language models (LLMs) often need to balance their internal parametric knowledge with external information, such as user beliefs and content from
arXiv:2511.18903v2 Announce Type: replace-cross Abstract: Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even
arXiv:2604.22506v1 Announce Type: new Abstract: Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compr
arXiv:2604.22631v1 Announce Type: new Abstract: Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gai
arXiv:2604.22062v1 Announce Type: new Abstract: There are 7,407 languages in the world. But, what about the languages that are not there in the world? Are humans so narrow minded that we don't care ab
arXiv:2604.22526v1 Announce Type: new Abstract: Wireless localization of permanent magnets enables occlusion-free guidance for medical interventions, yet its practical accuracy is fundamentally limite
arXiv:2604.22081v1 Announce Type: new Abstract: Most reinforcement-learning (RL) controllers used in continuous control are architecturally centralized: observations are compressed into a single laten
arXiv:2604.22411v1 Announce Type: new Abstract: Even when decoding with temperature T=0, large language models (LLMs) can produce divergent outputs for identical inputs. Recent work by Thinking Machin
arXiv:2602.05639v4 Announce Type: replace Abstract: We introduce Variational Joint Embedding (VJE), a reconstruction-free latent-variable framework for non-contrastive self-supervised learning in repr
arXiv:2604.22098v1 Announce Type: new Abstract: Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data
arXiv:2508.05633v2 Announce Type: replace-cross Abstract: Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interacti
arXiv:2505.14990v3 Announce Type: replace Abstract: Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent s
arXiv:2604.01907v2 Announce Type: replace-cross Abstract: Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this pap
arXiv:2604.22061v1 Announce Type: cross Abstract: Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significa
arXiv:2411.08027v3 Announce Type: replace-cross Abstract: Most learning-based approaches to complex physical reasoning sidestep the crucial problem of parameter identification (e.g., mass, friction) t
arXiv:2506.16494v3 Announce Type: replace Abstract: Electrocardiograms (ECGs) provide non-invasive measurements of heart activity and are established tools for detecting cardiac arrhythmias. Although
Last year we introduced new geospatial analytics capabilities integrated for BigQuery. Building on this, we announced an expanded suite of tools at Google Cloud Next ‘26, designed to help your busines
arXiv:2604.22031v1 Announce Type: cross Abstract: We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framewor
arXiv:2509.22166v4 Announce Type: replace-cross Abstract: The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N
arXiv:2604.21991v1 Announce Type: cross Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations
arXiv:2604.22239v1 Announce Type: cross Abstract: This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark fo
arXiv:2408.16286v5 Announce Type: replace Abstract: Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed
arXiv:2604.22730v1 Announce Type: cross Abstract: We investigate whether neural models trained exclusively on modern morphological data can recover cross-lingual lexical structure consistent with hist
arXiv:2510.25977v4 Announce Type: replace Abstract: Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainiu
arXiv:2509.20886v2 Announce Type: replace Abstract: Video sequences often contain structured noise and background artifacts that obscure dynamic content, posing challenges for accurate analysis and re
arXiv:2604.22240v1 Announce Type: new Abstract: Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on
arXiv:2604.22676v1 Announce Type: new Abstract: Graph neural networks achieve strong node-classification accuracy, but their learned message passing entangles ego attributes, neighborhood smoothing, h
arXiv:2604.22067v1 Announce Type: cross Abstract: Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to i
arXiv:2604.22350v1 Announce Type: new Abstract: Monocular visual odometry (VO) is a fundamental computer vision problem with applications in autonomous navigation, augmented reality and more. While de
arXiv:2411.04680v5 Announce Type: replace Abstract: Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As po
arXiv:2604.22076v1 Announce Type: cross Abstract: Large language models (LLMs) often memorize private information during training, raising serious privacy concerns. While machine unlearning has emerge
arXiv:2604.22640v1 Announce Type: cross Abstract: Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better gui
arXiv:2604.21965v1 Announce Type: new Abstract: Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Ca
arXiv:2604.22026v1 Announce Type: new Abstract: AI research pipelines now produce a growing share of publishable academic output, including work that meets existing peer-review standards for quality a
arXiv:2604.22162v1 Announce Type: new Abstract: Automated sports analysis demands robust multi-object tracking (MOT), yet segmentation-based methods often struggle with mask errors and ID switches in
arXiv:2511.14427v3 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Lear
arXiv:2603.02709v2 Announce Type: replace-cross Abstract: We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sens
arXiv:2510.05971v3 Announce Type: replace Abstract: The generalization of the Transformer architecture via MetaFormer has reshaped our understanding of its success in computer vision. By replacing sel
arXiv:2604.22072v1 Announce Type: cross Abstract: Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clien
arXiv:2604.22080v1 Announce Type: new Abstract: LLM-based agents are rapidly being adopted for scientific data analysis, automating tasks once limited by human time and expertise. This capability is o
arXiv:2604.22038v1 Announce Type: new Abstract: We define and investigate source-modality monitoring -- the ability of multimodal models to track and communicate the input source from which pieces of
arXiv:2604.22409v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence
arXiv:2604.22686v1 Announce Type: new Abstract: We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly pred
arXiv:2604.22649v1 Announce Type: cross Abstract: Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) re
arXiv:2604.22237v1 Announce Type: cross Abstract: Diagnosing student problem behaviors requires teachers to synthesize multifaceted information, identify behavioral categories, and plan intervention s
arXiv:2604.22351v1 Announce Type: cross Abstract: Mid-infrared astronomy from the ground faces critical challenges in accurately detecting and quantifying sources due to the dominant spatially and tim
arXiv:2604.22695v1 Announce Type: cross Abstract: Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to ch
arXiv:2506.17299v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become increasingly deployed in safety-critical applications, the lack of systematic methods to assess their v
arXiv:2604.22142v1 Announce Type: new Abstract: This study examines how large language model rewriting alters the style and narrative texture of personal narratives. It analyzes 300 personal narrative
arXiv:2604.22127v1 Announce Type: new Abstract: Hybrid language models that interleave attention with recurrent components are increasingly competitive with pure Transformers, yet standard LoRA practi