High-Rate Quantized Matrix Multiplication I
arXiv:2601.17187v2 Announce Type: replace-cross Abstract: This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of larg
Knowledge catalogue
arXiv:2601.17187v2 Announce Type: replace-cross Abstract: This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of larg
arXiv:2510.27258v2 Announce Type: replace-cross Abstract: The quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts. Linear-ti
arXiv:2605.12619v1 Announce Type: cross Abstract: The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypoth
arXiv:2605.13094v1 Announce Type: new Abstract: The local analysis is an established approach to the study of singularities and mobility of linkages. Key result of such analyses is a local picture of
arXiv:2605.12693v1 Announce Type: new Abstract: Decision-focused learning trains predictive models end-to-end against downstream decision loss, but online settings suffer delayed feedback: outcomes ma
arXiv:2605.13293v1 Announce Type: new Abstract: Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images rem
arXiv:2605.08320v1 Announce Type: cross Abstract: Monocular depth estimation (MDE) with self-supervised training approaches struggles in low-texture areas, where photometric losses may lead to ambiguo
arXiv:2605.12536v1 Announce Type: cross Abstract: The Free Energy Principle (FEP) is a leading framework for mathematically modeling self-organization and learning, while Integrated Information Theory
This post discusses implementing Physics-Informed Neural Networks (PINNs) to solve the 3D heat equation within a C++ WebAssembly engine for real-time aerospace applications. The work combines machine
arXiv:2605.13786v1 Announce Type: new Abstract: Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentati
arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may
arXiv:2605.12924v1 Announce Type: new Abstract: The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification
arXiv:2605.13813v1 Announce Type: new Abstract: Automated CT triage requires models that are simultaneously accurate across diverse pathologies and reliable under institutional shift. While Vision Tra
arXiv:2605.12772v1 Announce Type: new Abstract: Wu et al. (2026) showed that most frontier large language models (LLMs) recommend a sponsored, roughly twice-as-expensive flight when their system promp
arXiv:2510.18114v3 Announce Type: replace-cross Abstract: Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implemen
arXiv:2602.13155v2 Announce Type: replace Abstract: Neural networks, particularly message-passing neural networks (MPNNs), are increasingly used as heuristics for hard combinatorial optimization probl
arXiv:2605.13713v1 Announce Type: new Abstract: Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue spa
arXiv:2605.13741v1 Announce Type: cross Abstract: Scene graphs are becoming a standard representation for robot navigation, providing hierarchical geometric and semantic scene understanding. However,
arXiv:2605.13265v1 Announce Type: new Abstract: Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server, but the cut-layer interface int
arXiv:2511.10709v2 Announce Type: replace-cross Abstract: Machine learning models are used for pattern recognition analysis of big data, without direct human intervention. The task of unsupervised lea
arXiv:2605.13503v1 Announce Type: cross Abstract: A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natur
arXiv:2605.13188v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generat
arXiv:2510.19304v3 Announce Type: replace Abstract: Discrete diffusion models offer a promising alternative to autoregressive generation through parallel decoding, but they suffer from a sampling wall
arXiv:2605.12556v1 Announce Type: new Abstract: Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based
arXiv:2605.12570v1 Announce Type: new Abstract: The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging d
arXiv:2605.13218v1 Announce Type: new Abstract: Cancer is one of the leading causes of death worldwide, making the development of rapid, minimally invasive, label-free and scalable diagnostic strategi
arXiv:2512.16767v2 Announce Type: replace Abstract: Posing 3D characters is a fundamental task in computer graphics. However, existing paradigms, ranging from traditional auto-rigging to recent pose-c
arXiv:2605.12640v1 Announce Type: new Abstract: Panoptic segmentation requires the simultaneous recognition of countable thing instances and amorphous stuff regions, placing joint demands on long-rang
arXiv:2605.13754v1 Announce Type: new Abstract: In this paper, we study the problem of manipulation skill acquisition for performing construction activities consisting of repetitive tasks (e.g., build
arXiv:2605.13101v1 Announce Type: cross Abstract: Synthesis planning seeks an efficient sequence of chemical reactions that produce a target molecule. Typically, a pretrained single-step (autoregressi
arXiv:2605.13197v1 Announce Type: cross Abstract: Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. Howe
arXiv:2603.19185v2 Announce Type: replace Abstract: Synthetic data is often perceived as a silver-bullet solution to data anonymization and privacy-preserving data publishing. Drawn from generative mo
arXiv:2605.13806v1 Announce Type: cross Abstract: We study the query complexity of min-max optimization of a nonconvex-nonconcave function f over [0,1]^d imes [0,1]^d. We show that, given oracle acces
arXiv:2601.21975v2 Announce Type: replace Abstract: Recent work identifies a stated-revealed (SvR) preference gap in language models (LMs): a mismatch between the values models endorse and the choices
arXiv:2605.13225v1 Announce Type: new Abstract: For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many tim
arXiv:2605.13126v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) suffer from over-squashing in deep message passing, where information from exponentially growing neighborhoods is compres
arXiv:2605.13305v1 Announce Type: new Abstract: Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long hor
arXiv:2304.11193v2 Announce Type: replace-cross Abstract: Predicting the outcomes of robotic actions, often referred to as learning a world model, in complex environments remains a fundamental challen
arXiv:2605.12762v1 Announce Type: cross Abstract: Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that dri
arXiv:2605.12838v1 Announce Type: new Abstract: Tracking an interpretable emotional arc of a conversation via the sentiment of individual utterances processed as a whole is central to both understandi
arXiv:2605.12852v1 Announce Type: new Abstract: Pertussis booster vaccination produces immune responses that vary widely across individuals in both peak magnitude and long-term durability. These two p
arXiv:2605.13651v1 Announce Type: cross Abstract: Audio provides critical situational cues, yet current Audio Language Models (ALMs) face an attention bottleneck in long-form recordings where dominant
arXiv:2605.13366v1 Announce Type: new Abstract: Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is
arXiv:2605.13476v1 Announce Type: new Abstract: Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testin
arXiv:2505.22445v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel learning-based framework for 3D shape registration, which overcomes the challenges of significant non-rigid
Obama on the Iran nuclear deal today: “We pulled it off without firing a missile. We got 97% of their enriched uranium out. There’s no dispute that it worked. We didn’t have to kill a whole bunch of p
arXiv:2605.13086v1 Announce Type: new Abstract: This paper presents an object manipulation strategy for the Variable Topology Truss (VTT) system, a truss robot that comprises actuated truss members co
arXiv:2605.13018v1 Announce Type: new Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that first app
arXiv:2605.13025v1 Announce Type: new Abstract: We study the problem of learning Nash equilibria in offline two-player zero-sum Markov games. While existing approaches often rely on explicit pessimism
arXiv:2605.13146v1 Announce Type: cross Abstract: Artificial intelligence (AI) has transformed imaging inverse problems, from medical diagnostics to Earth observation. Yet deep neural networks can pro
arXiv:2605.12566v1 Announce Type: cross Abstract: The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspect
arXiv:2605.13448v1 Announce Type: cross Abstract: Diffusion models are often trained in low-dimensional latent spaces, which are then reused for related but shifted datasets. In this work, we study wh
arXiv:2605.12691v1 Announce Type: new Abstract: Progression, the task of updating a knowledge base to reflect action effects, generally requires second-order logic. Identifying first-order special cas
arXiv:2605.12753v1 Announce Type: cross Abstract: INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing re
arXiv:2602.17346v2 Announce Type: replace-cross Abstract: Preordering is a generalization of clustering and partial ordering with applications in bioinformatics and social network analysis. Given a fi
arXiv:2601.21366v2 Announce Type: replace Abstract: The forward pass of a Transformer can be seen as an interacting particle system on the unit sphere: time plays the role of layers, particles that of
arXiv:2605.13583v1 Announce Type: new Abstract: Recent advances have demonstrated that coded aperture snapshot spectral imaging (CASSI) systems show great potential for capturing 3D hyperspectral imag
arXiv:2605.13268v1 Announce Type: cross Abstract: Product formulas for Trotter Suzuki simulation remain a practical route to Hamiltonian evolution on noisy intermediate scale quantum (NISQ) hardware,
arXiv:2602.23089v2 Announce Type: replace Abstract: The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filte
arXiv:2506.19037v4 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) promise fast, non-autoregressive text generation, yet existing samplers, which pick tokens to unmask