Knowledge catalogue
arXiv:2605.01519v1 Announce Type: new Abstract: We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robust
arXiv:2605.00933v1 Announce Type: new Abstract: Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; eta-cell dysfunction), but population-scale deploy
arXiv:2605.01284v1 Announce Type: cross Abstract: Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions by progressively retrievi
arXiv:2605.02636v1 Announce Type: new Abstract: Near-infrared (NIR; a.k.a. NIRS) deep-learning studies in chemometrics increasingly report mutually inconsistent conclusions regarding convolutional neu
arXiv:2408.04596v2 Announce Type: replace Abstract: In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker al
arXiv:2601.21766v3 Announce Type: replace Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new fun
arXiv:2605.00908v1 Announce Type: new Abstract: This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in realistic
arXiv:2603.07615v2 Announce Type: replace-cross Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixel
arXiv:2603.14632v2 Announce Type: replace Abstract: The quality and realism of synthetically generated fingerprint images have increased significantly over the past decade fueled by advancements in ge
arXiv:2605.01270v1 Announce Type: new Abstract: Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structu
arXiv:2605.01073v1 Announce Type: new Abstract: The paper studies the local geometry of embedding clouds induced by controlled local classes of semantically close sentences. The central question is ho
arXiv:2509.01453v2 Announce Type: replace Abstract: Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image mem
arXiv:2605.02028v1 Announce Type: new Abstract: Large language models perform strongly on benchmarks in mathematical reasoning, coding and document analysis, suggesting a broad ability to follow instr
arXiv:2605.00074v1 Announce Type: cross Abstract: DNA-synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists. We show that this baseline collapses
arXiv:2602.15823v2 Announce Type: replace Abstract: A central challenge in large language model (LLM) editing is capability preservation: methods that successfully change targeted behavior can quietly
arXiv:2605.01815v1 Announce Type: new Abstract: Generative Adversarial Networks (GANs) offer a pragmatic route to mitigate data scarcity in vision tasks. We study generative augmentation across two lo
arXiv:2605.02153v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mappi
arXiv:2605.01479v1 Announce Type: new Abstract: Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free
arXiv:2605.00849v1 Announce Type: cross Abstract: Multi-antenna receiving systems have become a prevalent technical solution in communication systems. Meanwhile, deep learning has achieved significant
arXiv:2605.01067v1 Announce Type: new Abstract: Symbolic regression discovers explicit, interpretable equations without assuming a functional form in advance. A Bayesian approach strengthens this thro
arXiv:2602.17205v2 Announce Type: replace-cross Abstract: The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbo
arXiv:2605.02245v1 Announce Type: new Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep arch
arXiv:2605.02327v1 Announce Type: cross Abstract: We study the problem of denoising observations (Y_i=X_i+Z_i), where the latent variables (X_i) are sampled from a low-dimensional manifold in (R^n) an
arXiv:2605.02608v1 Announce Type: new Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-
arXiv:2604.01275v2 Announce Type: replace-cross Abstract: In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerica
arXiv:2605.02109v1 Announce Type: new Abstract: The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathe
arXiv:2511.13108v3 Announce Type: replace Abstract: The rapid progress of generative models such as GANs and diffusion models has led to the widespread proliferation of AI-generated images, raising co
arXiv:2605.00905v1 Announce Type: new Abstract: Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive
arXiv:2605.02715v1 Announce Type: cross Abstract: Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural a
arXiv:2605.00894v1 Announce Type: new Abstract: Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many cur
arXiv:2605.02417v1 Announce Type: new Abstract: With recent advancements in large-scale pre-trained text-to-image (T2I) models, training-free image editing methods have demonstrated remarkable success
arXiv:2605.01848v1 Announce Type: new Abstract: Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pat
arXiv:2411.14295v3 Announce Type: replace Abstract: Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video
arXiv:2605.00941v1 Announce Type: cross Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem. Existing
Alex Lupsasca from OpenAI discusses unconventional or intuitive approaches to physics research and problem-solving, likely exploring how intuition, visualization, and creative thinking ('vibe') comple
arXiv:2605.01852v1 Announce Type: new Abstract: Multi-view 3D reconstruction, namely, structure-from-motion followed by multi-view stereo, is a fundamental component of 3D computer vision. In general,
arXiv:2605.02416v1 Announce Type: cross Abstract: In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for LEO satellite networks. The pro
arXiv:2605.00327v1 Announce Type: cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferen
arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run
arXiv:2506.18315v2 Announce Type: replace-cross Abstract: LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies h
arXiv:2605.01799v1 Announce Type: new Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representat
arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated
arXiv:2605.01315v1 Announce Type: new Abstract: This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Usi
arXiv:2605.02378v1 Announce Type: new Abstract: In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile
arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-s
arXiv:2512.18273v2 Announce Type: replace-cross Abstract: Quantum error correction (QEC) for fault-tolerant quantum computing requires a balanced decoding solution that offers high performance, low co
arXiv:2605.01655v1 Announce Type: cross Abstract: We study homogeneous refinement operators ((Vgamma)(t)=sum_{jinmathbb Z}A_jgamma(Mt-j)), acting on compactly supported continuous piecewise linear cur
arXiv:2510.20103v2 Announce Type: replace-cross Abstract: The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its a
arXiv:2605.02393v1 Announce Type: new Abstract: Fashion design aims to express a designer's creative intent and to depict how garments interact with the human body. Recent methods condition on multimo
arXiv:2604.02019v2 Announce Type: replace Abstract: Pool-based sequential active learning for regression (ALR) optimally selects a small number of samples sequentially from a large pool of unlabeled s
arXiv:2605.02374v1 Announce Type: cross Abstract: Machine-generated text (MGT) detection is critical for regulating online information ecosystems, yet existing detectors often underperform in few-shot
arXiv:2605.01596v1 Announce Type: new Abstract: This paper describes the system submitted by team extbf{Archaeology} to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of
arXiv:2605.01702v1 Announce Type: new Abstract: Theoretical studies show that for any differentiable function on a compact domain, there exists a neural network that approximates both the function val
arXiv:2605.01199v1 Announce Type: new Abstract: Transformer-based models have achieved remarkable success across a wide range of domains, yet our understanding of their training dynamics remains limit
arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar
arXiv:2605.01367v1 Announce Type: cross Abstract: High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlate
arXiv:2605.01656v1 Announce Type: cross Abstract: Human cognition emerges from coordinated spiking dynamics in distributed neural circuits, where information is encoded via both firing rates and preci
arXiv:2605.00939v1 Announce Type: new Abstract: Traditional hallucination detection fails on 'Stubborn Hallucinations' -- errors where LLMs are confidently wrong. We propose a geometric solution: Embe
arXiv:2605.01616v1 Announce Type: new Abstract: Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartpho
arXiv:2605.02098v1 Announce Type: new Abstract: Large-scale 3D point clouds can consist of billions of points. Even after downsampling, these point clouds are too large for modern 3D neural networks.