Knowledge catalogue
arXiv:2606.08164v1 Announce Type: new Abstract: MRI preprocessing defines the input distribution seen by brain MRI foundation models, yet it is usually treated as routine data cleaning rather than a m
arXiv:2603.13259v2 Announce Type: replace-cross Abstract: When a decoder-only transformer is forced to process matched correct and incorrect single-token continuations of a factual query, the two path
arXiv:2504.02983v3 Announce Type: replace-cross Abstract: Metaphor and humor share a lot of common ground, and metaphor is one of the most common humorous mechanisms. This study focuses on the humorou
arXiv:2504.05349v4 Announce Type: replace-cross Abstract: Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most methods focus on empirical r
arXiv:2606.08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains
arXiv:2606.09589v1 Announce Type: cross Abstract: AI minidramas (also known as fruit dramas) are short, algorithmically distributed generative AI video series featuring anthropomorphized characters th
arXiv:2606.09347v1 Announce Type: new Abstract: Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by
arXiv:2606.07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent component
arXiv:2606.09111v1 Announce Type: new Abstract: Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however,
arXiv:2606.08144v1 Announce Type: new Abstract: Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing met
arXiv:2606.07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by
arXiv:2602.03682v2 Announce Type: replace-cross Abstract: We analyze the Accelerated Noisy Power Method, an algorithm for Principal Component Analysis in the setting where only inexact matrix-vector p
arXiv:2606.09154v1 Announce Type: new Abstract: Decentralized SGD is a fundamental algorithm in decentralized learning, although the influence of an underlying network topology on its convergence beha
arXiv:2606.07801v1 Announce Type: new Abstract: Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the c
arXiv:2601.05261v2 Announce Type: replace-cross Abstract: Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information
arXiv:2606.09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects
arXiv:2606.08831v1 Announce Type: new Abstract: Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node corre
arXiv:2606.08479v1 Announce Type: new Abstract: Inferring the forces that drive a dynamical system from partial observations is a fundamental challenge across physics, particularly when distinct under
arXiv:2606.07588v1 Announce Type: cross Abstract: We consider the black-box optimization problem on a sphere. Two information-geometric optimization flows (IGO flows) are designed with rigorous calcul
arXiv:2606.09052v1 Announce Type: cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet e
arXiv:2602.18695v2 Announce Type: replace Abstract: Existing insertion-based masked diffusion models that generate sequences by interleaving token insertion with unmasking use fixed schedules that are
arXiv:2606.07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; tem
arXiv:2603.08630v2 Announce Type: replace Abstract: We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor pro
arXiv:2606.09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supe
arXiv:2606.08858v1 Announce Type: cross Abstract: The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters ar
arXiv:2606.09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases. Yet in-posse
arXiv:2606.08850v1 Announce Type: cross Abstract: Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output select
arXiv:2402.13425v3 Announce Type: replace-cross Abstract: It is becoming increasingly common in regression to train neural networks that model the entire distribution even if only the mean is required
arXiv:2603.16020v2 Announce Type: replace Abstract: Adaptive agents operating under uncertainty must do more than optimize task outputs: they must maintain a workable internal state under noise, pertu
arXiv:2606.07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantiz
arXiv:2606.08816v1 Announce Type: cross Abstract: Predicting the effect of an unseen gene knockout perturbation on transcriptomic gene expression remains a highly challenging problem for virtual cell
arXiv:2510.09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minor
arXiv:2506.20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can har
arXiv:2606.09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the sa
arXiv:2606.09828v1 Announce Type: new Abstract: Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space.
arXiv:2606.08001v1 Announce Type: new Abstract: Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (O
arXiv:2606.09744v1 Announce Type: new Abstract: We study feed-forward ReLU networks with fixed readout and quadratic loss. The aim is to rewrite gradient descent not primarily as a dynamics in weight
arXiv:2606.08672v1 Announce Type: new Abstract: Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver lear
arXiv:2606.07932v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. Howev
arXiv:2606.09034v1 Announce Type: new Abstract: Neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) are two mainstream approaches for novel view synthesis. They often show complementary perf
arXiv:2606.07601v1 Announce Type: cross Abstract: We introduce the Laplace-Fourier Neural Operator (LFNO), a unified framework for modeling dynamical systems across transient and steady-state regimes
arXiv:2606.07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and s
arXiv:2512.20591v3 Announce Type: replace Abstract: Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. Howeve
arXiv:2606.07670v1 Announce Type: cross Abstract: Deformable 3D Gaussian Splatting (D-3DGS) re-constructs dynamic scenes from monocular video by deforming a canonical set of 3D Gaussians through a pos
arXiv:2606.08511v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long vis
arXiv:2606.09480v1 Announce Type: new Abstract: Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic
arXiv:2606.08676v1 Announce Type: cross Abstract: AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of the
arXiv:2606.09313v1 Announce Type: new Abstract: Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solv
arXiv:2606.07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural net
arXiv:2009.10277v2 Announce Type: replace-cross Abstract: We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combini
arXiv:2606.07614v1 Announce Type: new Abstract: Reliable measurement of income and consumption is essential for monitoring poverty and inequality in low- and middle-income countries, yet full househol
arXiv:2601.21996v2 Announce Type: replace-cross Abstract: While Mechanistic Interpretability has identified interpretable circuits in LLMs, their causal origins in training data remain elusive. We int
arXiv:2606.09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening qualit
arXiv:2606.09827v1 Announce Type: cross Abstract: Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future stat
arXiv:2606.08287v1 Announce Type: new Abstract: Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design ite
arXiv:2606.09056v1 Announce Type: new Abstract: Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames req
arXiv:2510.05356v2 Announce Type: replace Abstract: Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score f
arXiv:2606.07654v1 Announce Type: cross Abstract: Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language
arXiv:2606.09645v1 Announce Type: new Abstract: Text based configuration files for cyber-physical systems show the hierarchy of component modules well but often hide the details of connections and int
arXiv:2601.21522v2 Announce Type: replace-cross Abstract: The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question cor