Knowledge catalogue
Reuters: Meta is installing tracking software on US staffers' computers to capture mouse movements, clicks, and keystrokes in work-related apps for use in AI training — Meta (META.O) is installing new
arXiv:2604.17982v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) still struggle with vision hallucination, where generated responses are inconsistent with the visual input. Exist
arXiv:2604.17007v1 Announce Type: new Abstract: Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present
As AI assistants and privacy proxies challenge the capabilities of traditional bot detection, the Web needs new models for accountability. We believe that control should remain with the client, and th
arXiv:2401.15604v4 Announce Type: replace Abstract: Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component. While r
arXiv:2509.26010v2 Announce Type: replace Abstract: Second-order PDE models have been widely used for suppressing multiplicative noise, but they often introduce blocky artifacts in the early stages of
arXiv:2604.16878v1 Announce Type: new Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acu
arXiv:2511.14582v2 Announce Type: replace Abstract: Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However,
arXiv:2502.08531v3 Announce Type: replace Abstract: Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variable
arXiv:2604.18393v1 Announce Type: new Abstract: Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under t
arXiv:2604.17984v1 Announce Type: new Abstract: Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. In particular, we consider the problem
arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation
arXiv:2508.14461v3 Announce Type: replace Abstract: While multi-step diffusion models have advanced both forward and inverse rendering, existing approaches often treat these problems independently, le
arXiv:2511.21064v2 Announce Type: replace-cross Abstract: Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although
arXiv:2604.18548v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-info
arXiv:2505.15342v2 Announce Type: replace-cross Abstract: We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model wi
arXiv:2604.16519v1 Announce Type: new Abstract: In the field of online reinforcement learning (RL), traditional Gaussian policies and flow-based methods are often constrained by their unimodal express
arXiv:2510.18109v4 Announce Type: replace-cross Abstract: Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model bu
arXiv:2604.17290v1 Announce Type: new Abstract: LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to rea
arXiv:2603.19830v2 Announce Type: replace Abstract: Efficient structural perception is essential for mapping and autonomous navigation on resource-constrained robots. Existing 3D methods are computati
arXiv:2604.18575v1 Announce Type: new Abstract: Story Visualization aims to generate a sequence of images that faithfully depicts a textual narrative that preserve character identity, spatial configur
arXiv:2604.16677v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructi
arXiv:2510.23116v3 Announce Type: replace Abstract: Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restora
arXiv:2604.16453v1 Announce Type: new Abstract: We introduce a principled probabilistic framework for reward-guided decoding in large language models, addressing the limitations of standard decoding m
arXiv:2411.15115v3 Announce Type: replace-cross Abstract: Recent text-to-video (T2V) diffusion models have made remarkable progress in generating high-quality videos. However, they often struggle to a
arXiv:2604.16817v1 Announce Type: new Abstract: Imbalanced data is commonly present in real-world applications. While data synthesis can effectively mitigate the data scarcity problem of rare-classes,
arXiv:2604.17652v1 Announce Type: new Abstract: Sentinel-5P (S5P) plays a critical role in atmospheric monitoring; however, its spatial resolution limits fine-scale analysis. Existing super-resolution
arXiv:2406.06543v2 Announce Type: replace-cross Abstract: Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devic
arXiv:2604.17005v1 Announce Type: new Abstract: Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semant
arXiv:2506.10060v2 Announce Type: replace Abstract: Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncert
arXiv:2604.16442v1 Announce Type: cross Abstract: Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while
Database powerhouse Oracle Corp. is making its case that database architecture must serve as a trusted foundation for the AI that gets built on top. The company’s major step into the AI database as a
arXiv:2603.08096v3 Announce Type: replace Abstract: Localizing objects and parts from natural language in 3D space is essential for robotics, AR, and embodied AI, yet existing methods face a trade-off
arXiv:2405.13068v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Cu
arXiv:2507.21545v3 Announce Type: replace Abstract: Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer st
arXiv:2604.18035v1 Announce Type: new Abstract: Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a differen
arXiv:2601.08184v3 Announce Type: replace-cross Abstract: Non-asymptotic central limit theorem (CLT) rates play a central role in modern machine learning and operations research. In this paper, we stu
arXiv:2604.17050v1 Announce Type: new Abstract: With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment c
We're open-sourcing FlashKDA — our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20,
arXiv:2602.17909v2 Announce Type: replace Abstract: Diffusion-based approaches have recently demonstrated strong performance for single-image novel view synthesis by conditioning generative models on
arXiv:2604.15609v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) for black-box models accessible only via APIs remains a largely unexplored challenge. Existing approaches such as post-hoc
arXiv:2503.18970v3 Announce Type: replace Abstract: Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Ne
arXiv:2604.16207v1 Announce Type: cross Abstract: As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods t
arXiv:2604.16015v1 Announce Type: cross Abstract: Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent
arXiv:2604.16044v1 Announce Type: new Abstract: Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these mo
arXiv:2604.16238v1 Announce Type: new Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such for
arXiv:2604.15619v1 Announce Type: new Abstract: Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of suc
arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode
arXiv:2604.15395v1 Announce Type: new Abstract: Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towa
arXiv:2604.15642v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rap
arXiv:2512.02543v2 Announce Type: replace Abstract: Deploying LLM agents at scale typically requires choosing between quality and cost. Existing cost-reduction approaches fail to preserve agility: the
arXiv:2603.11331v2 Announce Type: replace-cross Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that strong adversari
arXiv:2604.15729v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis is pivotal in computational pathology, enabling cancer diagnosis by integrating morphological and architectural cues
arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data
arXiv:2507.20058v3 Announce Type: replace-cross Abstract: Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive met
arXiv:2509.17292v3 Announce Type: replace-cross Abstract: Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual
arXiv:2604.15694v1 Announce Type: new Abstract: Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet exi
arXiv:2604.16021v1 Announce Type: cross Abstract: Code localization is a cornerstone of autonomous software engineering. Recent advancements have achieved impressive performance on real-world issue be
arXiv:2604.16146v1 Announce Type: new Abstract: Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned)
arXiv:2604.15364v1 Announce Type: cross Abstract: Edge intelligence is constrained by the energy and latency costs of shuttling data through electronic memory hierarchies. Optical systems offer a fund