Knowledge catalogue
arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This r
arXiv:2609.39738v1 Announce Type: new Abstract: Learning to generate machining process plans and toolpaths from B-rep CAD requires coupling discrete operation decisions with continuous tool motion as
arXiv:2609.39784v1 Announce Type: new Abstract: Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system r
arXiv:2609.39955v1 Announce Type: new Abstract: Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among
arXiv:2609.39924v1 Announce Type: cross Abstract: Vision-language models face a fundamental scaling bottleneck: the number of visual tokens grows with both temporal duration and spatial resolution, ma
arXiv:2609.38476v1 Announce Type: new Abstract: Synthetic data are most valuable where general-purpose datasets cannot provide the domain-specific priors a task requires, and where manual annotation i
arXiv:2508.12145v5 Announce Type: replace Abstract: Recently, autoencoders (AEs) have gained interest for creating parametric and invertible projections of multidimensional data. Parametric projection
arXiv:2505.05855v4 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pai
arXiv:2609.38856v1 Announce Type: new Abstract: The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent featu
arXiv:2601.21857v2 Announce Type: replace Abstract: We revisit diffusion-based generation for structured visual content and identify a fundamental limitation of existing approaches: foreground preserv
arXiv:2609.39755v1 Announce Type: new Abstract: Safe and efficient quadruped navigation over unfamiliar terrain requires predicting terrain-robot interaction before contact: geometry and visual appear
arXiv:2609.38594v1 Announce Type: new Abstract: Group synchronization (GS) is the problem of estimating a set of N unknown elements g_1,ldots, g_N in G in a group G, given noisy measurements of a subs
arXiv:2609.39863v1 Announce Type: new Abstract: Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing w
arXiv:2609.39383v1 Announce Type: cross Abstract: Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics, pairing design rationales with executab
arXiv:2609.38332v1 Announce Type: cross Abstract: Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context
arXiv:2609.39127v1 Announce Type: new Abstract: Satellite-ground localization estimates the planar position and yaw orientation of a ground camera within a geo-referenced satellite image. Most recent
arXiv:2609.39380v1 Announce Type: new Abstract: Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one elemen
arXiv:2609.39627v1 Announce Type: new Abstract: This book presents a code-first introduction to computer vision, spanning classical 2D image processing, classical 3D vision, and deep learning. Organiz
arXiv:2609.38842v1 Announce Type: cross Abstract: Learn-then-differentiate (LTD) estimates gradients by fitting a model to simulation outputs and differentiating it. We develop a unified framework exp
arXiv:2609.39220v1 Announce Type: new Abstract: Process reward models (PRMs) have demonstrated notable effectiveness in test-time scaling and reinforcement learning by providing fine-grained signals f
arXiv:2609.40219v1 Announce Type: cross Abstract: World Action Models (WAMs) couple visual dynamics prediction with action generation, yet they do not explicitly support the reuse of action experience
arXiv:2609.39838v1 Announce Type: new Abstract: Chain-of-thought monitoring as an approach for AI oversight and control is threatened by the possibility of steganographic reasoning, where LLMs conceal
arXiv:2609.38920v1 Announce Type: new Abstract: Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives.
arXiv:2609.39038v1 Announce Type: new Abstract: Generative policies have emerged as a promising paradigm for robot learning, combining expressive generative action modeling with scalable imitation lea
arXiv:2609.38746v1 Announce Type: new Abstract: How can a 3D reconstruction system acquire and retain useful information to understand the geometry of a scene from partial views under a limited comput
arXiv:2609.39628v1 Announce Type: new Abstract: This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint dis
arXiv:2609.39020v1 Announce Type: cross Abstract: We study how well deep neural networks approximate and learn spectral Barron functions. Recent studies have shown that these function classes can be e
arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects maliciou
arXiv:2609.39832v1 Announce Type: new Abstract: Many visual tracking methods use rejection mechanisms to suppress unreliable predictions. However, these mechanisms can also reject correctly localized
arXiv:2609.38466v1 Announce Type: new Abstract: Text-conditioned full-body human-object interaction (HOI) generation requires synthesizing human motion and object trajectories that match the input tex
arXiv:2609.38926v1 Announce Type: cross Abstract: Long-term precipitation nowcasting requires modeling radar-echo evolution while preserving localized high-intensity structures. Recent radar-specific
arXiv:2609.39848v1 Announce Type: new Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation
arXiv:2609.38632v1 Announce Type: new Abstract: Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble membe
arXiv:2410.20317v2 Announce Type: replace Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making
arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although re
arXiv:2609.40090v1 Announce Type: new Abstract: Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must tr
arXiv:2503.19081v2 Announce Type: replace Abstract: Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDE
arXiv:2302.02006v2 Announce Type: replace Abstract: Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inheren
arXiv:2609.39007v1 Announce Type: cross Abstract: Sessionized interaction histories contain behavioral patterns that can improve sequential recommendation. However, existing models process all session
arXiv:2609.39635v1 Announce Type: new Abstract: Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates extit{salient} fa
arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m
With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so r
arXiv:2609.39590v1 Announce Type: new Abstract: Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-conditi
arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vie
arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,
arXiv:2609.40083v1 Announce Type: cross Abstract: One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by a
arXiv:2511.17735v2 Announce Type: replace Abstract: Foundation models in several scientific domains, including visual domains, learn representations that capture complex semantics from their respectiv
arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generate
arXiv:2609.38635v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is a popular representation for novel view synthesis. However, 3DGS contains millions of Gaussian primitives, each with r
arXiv:2609.38446v1 Announce Type: cross Abstract: A reward can identify a correct answer while leaving the computation needed for new inputs undetermined. We study how pretraining and midtraining supp
arXiv:2609.38275v1 Announce Type: cross Abstract: Persistent memory helps LLM agents carry information across long interactions, but correct memory can still be used incorrectly when queries change or
arXiv:2609.39604v1 Announce Type: new Abstract: Although conventional world models - auto-regressive or diffusion models based on transformers or convolutional networks - may learn surface statistics
arXiv:2609.36639v1 Announce Type: new Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in discov
arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly r
arXiv:2609.37229v1 Announce Type: new Abstract: Human motion defines an action, while a camera trajectory determines how it is presented. Camera generation for a given human motion and joint human-cam
arXiv:2609.35835v1 Announce Type: cross Abstract: With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is wheth
arXiv:2609.37441v1 Announce Type: new Abstract: Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal represen
arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical sta
arXiv:2609.36609v1 Announce Type: cross Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are hi
arXiv:2609.36305v1 Announce Type: new Abstract: World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we pro