Knowledge catalogue
arXiv:2609.07816v1 Announce Type: new Abstract: Linear attention is increasingly used in frontier language models for efficient long-context inference and constant-memory decoding. Its fixed-size recu
arXiv:2609.08135v1 Announce Type: new Abstract: We develop a second-order theory of quantization noise in matrix multiplication in which the quantization format is characterized by the variance it ass
arXiv:2603.01875v4 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teach
arXiv:2609.05540v1 Announce Type: new Abstract: Many medical conditions require diagnosis through detailed, multi-context clinical assessment rather than from visual appearance alone. Despite this, vi
arXiv:2609.06522v1 Announce Type: new Abstract: A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm. The low
arXiv:2509.04772v2 Announce Type: replace Abstract: Timely floodwater depth estimates support road accessibility assessment and emergency response during urban flooding. Supervised vision methods ofte
arXiv:2609.07298v1 Announce Type: new Abstract: 3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by whether co
arXiv:2609.07240v1 Announce Type: new Abstract: Here we investigate the stability of the Kolmogorov--Arnold representation theorem (KART) under adversarial reparameterisations of the hidden layer for
arXiv:2402.01306v5 Announce Type: replace Abstract: Kahneman & Tversky's extit{prospect theory} tells us that humans perceive random variables in a biased but well-defined manner (1992); for example,
arXiv:2503.02578v2 Announce Type: replace Abstract: Offline high-definition maps provide essential geometric and topological priors for autonomous driving systems. Pure-vision solutions have become th
arXiv:2512.03619v4 Announce Type: replace Abstract: Video generation has achieved remarkable progress in visual fidelity and controllability, enabling conditioning on text, layout, or motion. Among th
arXiv:2609.06368v1 Announce Type: new Abstract: We present LANTERN, a closed-loop benchmark for temporally grounded cooperative warnings. LANTERN separates warning onset, hazard onset, warning termina
arXiv:2609.06873v1 Announce Type: cross Abstract: We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification
arXiv:2609.07049v1 Announce Type: cross Abstract: Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret
arXiv:2609.06437v1 Announce Type: new Abstract: Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods oft
arXiv:2609.06733v1 Announce Type: new Abstract: Brain tumor segmentation from multimodal MRI relies on complementary evidence across four imaging sequences, yet one or more modalities may be unavailab
Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 ChatGPT can already read PDFs. But document work gets harder when the information you need is buried in messy scans, den
arXiv:2310.18727v2 Announce Type: replace Abstract: The latent class model is a highly effective tool in the analysis of categorical data from social, psychological, and behavioral sciences, where pop
arXiv:2609.07814v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of st
arXiv:2609.07460v1 Announce Type: new Abstract: Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in thi
arXiv:2512.04821v4 Announce Type: replace Abstract: Generative models have achieved remarkable progress with the emergence of flow matching (FM). It has demonstrated strong generative capabilities and
arXiv:2609.06993v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipeline
arXiv:2609.06761v1 Announce Type: new Abstract: While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharact
arXiv:2609.08524v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts arise across institutions, acquisition pro
arXiv:2609.06590v1 Announce Type: cross Abstract: Mixture of Prompt Experts (MoPE) adapts multimodal transformers through input-dependent prompt composition, while retaining a fixed prompt length. We
arXiv:2609.06079v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hie
arXiv:2604.05819v2 Announce Type: replace Abstract: Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains
arXiv:2508.18817v3 Announce Type: replace-cross Abstract: Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions,
arXiv:2609.06881v1 Announce Type: new Abstract: We study a two-server load balancing system with heterogeneous service rates that are a priori unknown to the dispatcher. The goal is to route customers
arXiv:2602.13174v2 Announce Type: replace Abstract: Partial differential equation (PDE) models frequently contain unknown functional terms that cannot be measured directly, limiting their predictive u
arXiv:2609.06474v1 Announce Type: new Abstract: Kernel methods separate data representation from decision-making, but typically require the kernel to be chosen in advance. We show that this kernel can
arXiv:2609.09157v1 Announce Type: new Abstract: Recurrent models provide a natural path to long-context modeling, yet models trained with backpropagation through time (BPTT) often fail beyond their tr
arXiv:2609.08102v1 Announce Type: new Abstract: Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems. However, eigenvalue problems, in w
arXiv:2510.02532v2 Announce Type: replace-cross Abstract: Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimension
arXiv:2602.22061v3 Announce Type: replace-cross Abstract: Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physi
arXiv:2607.11140v2 Announce Type: replace Abstract: Determining whether one finite group is isomorphic to a subgroup of another is a fundamental problem in computational group theory. In this work, we
arXiv:2609.08669v1 Announce Type: cross Abstract: Robotic construction offers the potential to use materials more efficiently and create complex geometries, but current methods rely on rigid, high-pre
arXiv:2609.06083v1 Announce Type: new Abstract: To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market
arXiv:2609.06578v1 Announce Type: new Abstract: World Action Models (WAMs) extend Vision-Language-Action (VLA) models by incorporating future visual dynamics into action generation. However, existing
arXiv:2609.08851v1 Announce Type: new Abstract: Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particula
arXiv:2609.09009v1 Announce Type: new Abstract: Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the
arXiv:2609.08582v1 Announce Type: new Abstract: Chagas disease is a major cause of cardiomyopathy in Latin America. Cardiac magnetic resonance (CMR) imaging can characterize its structural abnormaliti
arXiv:2609.08622v1 Announce Type: new Abstract: Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represents t
arXiv:2511.03032v3 Announce Type: replace Abstract: In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For exam
arXiv:2609.08841v1 Announce Type: new Abstract: High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail s
Lightbits Labs Ltd. today announced the general availability of Inferra, a software engine designed to improve the economics and performance of artificial intelligence inference by moving key-value ca
arXiv:2609.07274v1 Announce Type: cross Abstract: SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. Ho
arXiv:2512.13271v2 Announce Type: replace Abstract: Cable-driven continuum robots (CDCRs) require accurate, real-time dynamic models for high-speed dynamics prediction or model-based control, making s
arXiv:2609.06686v1 Announce Type: cross Abstract: We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation. A normalizi
arXiv:2609.08585v1 Announce Type: cross Abstract: Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs
arXiv:2609.06341v1 Announce Type: new Abstract: Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial intel
arXiv:1906.02590v2 Announce Type: replace-cross Abstract: This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods
Automated lint: 12 errors, 53990 warnings, 3 info
Livestream Alert: Comfy Agent Beta: VFX, Marketing & E-Commerce Workflows at 10am PT Comfy Agent is entering beta, and we're opening the door! Join us live as the team behind it walks through what the
arXiv:2609.05559v1 Announce Type: cross Abstract: We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a v
arXiv:2609.07876v1 Announce Type: cross Abstract: Recent methods in language model interpretability employ techniques such as sparse autoencoders to decompose residual stream contributions into linear
arXiv:2609.06467v1 Announce Type: new Abstract: In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution con
arXiv:2609.07752v1 Announce Type: new Abstract: Field temporal prediction and source identification constitute canonical problems in dynamical systems. Conventional approaches to these problems depend
arXiv:2609.08408v1 Announce Type: new Abstract: Focusing on spatially localized, control-relevant visual cues has been shown to improve data efficiency in visuomotor policies by reducing the need to m
arXiv:2609.07029v1 Announce Type: new Abstract: Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from