Knowledge catalogue
arXiv:2604.10040v1 Announce Type: new Abstract: The primary goal of this work is to systematically evaluate the intra-finger variability of synthetic fingerprints (particularly latent prints) generate
arXiv:2511.08659v3 Announce Type: replace-cross Abstract: This book is an introductory textbook targeted towards computer science students who are completely new to the topic of automated negotiation.
arXiv:2604.11035v1 Announce Type: new Abstract: Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of intr
arXiv:2512.23532v2 Announce Type: replace Abstract: Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency
arXiv:2604.09922v1 Announce Type: cross Abstract: Subsurface stratigraphy contains important spatio-temporal information about accumulation, deformation, and layer formation in polar ice sheets. In pa
arXiv:2604.09800v1 Announce Type: new Abstract: This paper presents an analytical framework to study the geometry arising when a soft continuum arm grasps a planar object. Both the arm centerline and
arXiv:2506.23471v2 Announce Type: replace-cross Abstract: The global fashion e-commerce industry has become integral to people's daily lives, leveraging technological advancements to offer personalize
arXiv:2509.20128v2 Announce Type: replace-cross Abstract: Audio-driven facial animation has made significant progress in multimedia applications, with diffusion models showing strong potential for tal
arXiv:2506.06248v2 Announce Type: replace Abstract: Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational descri
arXiv:2604.11748v1 Announce Type: new Abstract: Continuous diffusion models have achieved strong performance across domains such as images. However, in language modeling, prior continuous diffusion la
arXiv:2603.10079v2 Announce Type: replace Abstract: Large loss spikes in stochastic gradient descent are studied through a rigorous large-deviations analysis for a shallow, fully connected network in
arXiv:2604.09909v1 Announce Type: new Abstract: We study last-iterate convergence of SGD with greedy step size over smooth quadratics in the interpolation regime, a setting which captures the classica
arXiv:2603.02028v2 Announce Type: replace Abstract: Vision transformers have shown outstanding performance in image generation, yet their adoption in fluid dynamics remains limited. We introduce the L
arXiv:2604.11091v1 Announce Type: new Abstract: Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-leve
arXiv:2604.10573v1 Announce Type: new Abstract: Robust 3D representation learning forms the perceptual foundation of spatial intelligence, enabling downstream tasks in scene understanding and embodied
arXiv:2604.11311v1 Announce Type: new Abstract: Diffusion-based models on continuous spaces have seen substantial recent progress through the mathematical framework of gradient flows, leveraging the W
arXiv:2604.11473v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static
arXiv:2604.09664v1 Announce Type: cross Abstract: The non-equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but ac
arXiv:2511.18322v3 Announce Type: replace-cross Abstract: Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumpt
arXiv:2604.09566v1 Announce Type: cross Abstract: The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game d
arXiv:2604.11071v1 Announce Type: cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fe
arXiv:2407.05087v2 Announce Type: replace-cross Abstract: Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicati
arXiv:2604.09555v1 Announce Type: new Abstract: Multi-criteria Analysis (MCA) is used to rank alternatives based on various criteria. Key MCA methods, such as Multiple Criteria Decision Making (MCDM)
arXiv:2506.14493v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can ex
arXiv:2604.10927v1 Announce Type: new Abstract: We propose LiveGesture, the first fully streamable, speech-driven full-body gesture generation framework that operates with zero look-ahead and supports
arXiv:2601.10775v2 Announce Type: replace Abstract: We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with Tic-Tac-Toe. The method integrates in-conte
arXiv:2604.10834v1 Announce Type: cross Abstract: [Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because
arXiv:2604.11789v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks req
arXiv:2510.12117v3 Announce Type: replace-cross Abstract: Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for
arXiv:2604.10643v1 Announce Type: new Abstract: Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is
arXiv:2504.14386v2 Announce Type: replace-cross Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permut
arXiv:2604.10556v1 Announce Type: new Abstract: While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their fa
arXiv:2604.10765v1 Announce Type: cross Abstract: Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15%
arXiv:2303.10894v3 Announce Type: replace Abstract: Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wi
arXiv:2604.10021v1 Announce Type: cross Abstract: Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the fi
arXiv:2408.11871v4 Announce Type: replace-cross Abstract: Fake news significantly influences decision-making processes by misleading individuals, organizations, and even governments. Large language mo
arXiv:2604.10424v1 Announce Type: new Abstract: Foundation-style ECG encoders pretrained with self-supervised learning are increasingly reused across tasks, institutions, and deployment contexts, ofte
arXiv:2601.04448v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables b
arXiv:2604.11427v1 Announce Type: cross Abstract: Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose ours, a metho
arXiv:2604.10208v1 Announce Type: new Abstract: Asymmetric Tensor PCA (ATPCA) is a prototypical model for studying the trade-offs between sample complexity, computation, and memory. Existing algorithm
arXiv:2604.10095v1 Announce Type: new Abstract: With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning p
arXiv:2604.10636v1 Announce Type: new Abstract: Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with
arXiv:2604.11575v1 Announce Type: new Abstract: Pixel-based language models are gaining momentum as alternatives to traditional token-based approaches, promising to circumvent tokenization challenges.
arXiv:2604.11295v1 Announce Type: new Abstract: This paper proposes novel passive-dynamic walkers formed by two cross-shaped frames and eight viscoelastic elements. Since it is a combination of two fo
arXiv:2509.22403v2 Announce Type: replace Abstract: Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale an
arXiv:2604.11372v1 Announce Type: new Abstract: Crowd-sourced cooperative mapping from monocular cameras promises scalable 3D reconstruction without specialized sensors, yet remains hindered by two sc
arXiv:2505.23606v4 Announce Type: replace-cross Abstract: Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reas
arXiv:2510.02050v3 Announce Type: replace-cross Abstract: Improving statistical forecasts of tropical cyclone (TC) intensity is limited by complex nonlinear interactions and difficulty in identifying
arXiv:2604.10632v1 Announce Type: cross Abstract: Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. W
arXiv:2604.10359v1 Announce Type: cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. St
arXiv:2603.02123v3 Announce Type: replace Abstract: The development of affective multimodal language models (MLMs) has long been constrained by a gap between low-level perception and high-level intera
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. Just before Artemis I
先日 @Nature 誌に掲載された AI Scientist 論文の主要著者の一人、Sakana AI の山田祐太朗への取材記事が日経デジタルガバナンスに掲載されました。 https://www.nikkei.com/prime/digital-governance/article/DGXZQOSG305D90Q6A330C2000000 本記事では、AIによる科学研究の現在地について、AI S
arXiv:2405.16091v2 Announce Type: replace Abstract: Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explor
arXiv:2604.03874v2 Announce Type: replace Abstract: Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density
arXiv:2604.09817v1 Announce Type: new Abstract: Visual encoding and decoding models act as gateways to understanding the neural mechanisms underlying human visual perception. Typically, visual encodin
arXiv:2604.11172v1 Announce Type: cross Abstract: Direct volume rendering (DVR) aims to help users identify and examine regions of interest (ROIs) within volumetric data, and feature representations t
arXiv:2604.02340v2 Announce Type: replace-cross Abstract: Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive
arXiv:2604.11508v1 Announce Type: cross Abstract: Fine-tuning pretrained image classifiers is standard practice, yet which individual samples are forgotten during this process, and whether forgetting
arXiv:2604.10604v1 Announce Type: cross Abstract: Standard dense retrievers lack a native calculus for multi-atom logical constraints. We introduce Neuro-Symbolic Fuzzy Logic (NSFL), a framework that