Knowledge catalogue
arXiv:2604.13235v1 Announce Type: new Abstract: Digital elevation modeling of planetary surfaces is essential for studying past and ongoing geological processes. Wide-angle imagery acquired during spa
arXiv:2601.11340v2 Announce Type: replace Abstract: Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models gener
arXiv:2604.14021v1 Announce Type: new Abstract: Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide
arXiv:2604.13681v1 Announce Type: cross Abstract: The node2vec random walk is a non-Markovian random walk on the vertex set of a graph, widely used for network embedding and exploration. This random w
arXiv:2604.13073v1 Announce Type: new Abstract: Modern multimodal large language models (MLLMs) generate fluent responses from interleaved text, image, audio, and video inputs. However, identifying wh
arXiv:2408.10610v5 Announce Type: replace Abstract: We propose an L^2 norm for stationary Autoregressive Moving Average (ARMA) models. We look at ARMA models within the Hilbert space of the past with
arXiv:2604.14062v1 Announce Type: new Abstract: Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as triplets. Existing approaches split
arXiv:2604.13455v1 Announce Type: new Abstract: Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluc
arXiv:2604.13294v1 Announce Type: new Abstract: Existing video coding for machines is often trained for a specific downstream task and model. As a result, the compressed representation becomes tightly
arXiv:2604.13419v1 Announce Type: new Abstract: Noncontact exfiltration of electronic screen content poses a security challenge, with side-channel incursions as the principal vector. We introduce an o
arXiv:2604.13992v1 Announce Type: new Abstract: Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfe
arXiv:2604.13723v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet m
arXiv:2604.13291v1 Announce Type: new Abstract: Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal en
arXiv:2604.13986v1 Announce Type: new Abstract: Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, m
arXiv:2604.13966v1 Announce Type: new Abstract: We study value adaptation in offline-to-online reinforcement learning under general function approximation. Starting from an imperfect offline pretraine
arXiv:2604.13395v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LR
arXiv:2604.13786v1 Announce Type: new Abstract: As large language models become standard backends for content generation, practical provenance increasingly requires multi-bit watermarking. In provider
arXiv:2604.13951v1 Announce Type: new Abstract: This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analy
arXiv:2604.13492v1 Announce Type: cross Abstract: Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM). However, most r
arXiv:2604.13830v1 Announce Type: cross Abstract: Integro-differential equations arise in a wide range of applications, including transport, kinetic theory, radiative transfer, and multiphysics modeli
arXiv:2604.13735v1 Announce Type: cross Abstract: This work identifies a necessary condition for any variational quantum approach to reach the exact ground state. Briefly, the norms of the projections
arXiv:2604.13549v1 Announce Type: new Abstract: The conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision, bridging the gap between human creativity and digi
arXiv:2603.28942v3 Announce Type: replace Abstract: The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent propens
arXiv:2604.13326v1 Announce Type: new Abstract: The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A
arXiv:2604.13476v1 Announce Type: cross Abstract: Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as tele
arXiv:2604.13806v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces
arXiv:2510.18165v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) are emerging as a powerful and promising alternative to the dominant autoregressive paradigm, offering inhere
arXiv:2506.06114v4 Announce Type: replace Abstract: Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure mean
arXiv:2604.14026v1 Announce Type: new Abstract: Object extraction tasks often occur in disassembly problems, where bolts, screws, or pins have to be removed from tight, narrow spaces. In such problems
arXiv:2604.13941v1 Announce Type: new Abstract: Local feature matching plays a critical role in understanding the correspondence between cross-view images. However, traditional methods are constrained
arXiv:2604.13335v1 Announce Type: new Abstract: We introduce SEDTalker, an emotion-aware framework for speech-driven 3D facial animation that leverages frame-level speech emotion diarization to achiev
arXiv:2505.24869v3 Announce Type: replace Abstract: Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve high
arXiv:2604.13861v1 Announce Type: new Abstract: This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket namely batting orde
arXiv:2604.13405v1 Announce Type: new Abstract: Singular configurations cause loss of task-space mobility, unbounded joint velocities, and solver divergence in inverse kinematics (IK) for serial manip
arXiv:2604.13295v1 Announce Type: new Abstract: t-SNE has gained popularity as a dimension reduction technique, especially for visualizing data. It is well-known that all dimension reduction technique
arXiv:2601.22795v2 Announce Type: replace Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs. Several studi
arXiv:2604.13333v1 Announce Type: new Abstract: We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photore
arXiv:2604.13297v1 Announce Type: cross Abstract: This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and sta
arXiv:2602.06264v2 Announce Type: replace Abstract: We consider the problem of minimizing different notions of swap regret in online optimization. These forms of regret are tightly connected to correl
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Cyberscammers are bypassing banks’ security with illicit tools
arXiv:2505.12836v2 Announce Type: replace-cross Abstract: We consider the problem of sampling from a product-of-experts-type model that encompasses many standard prior and posterior distributions comm
As a movement, environmentalism has been pretty misanthropic. Understandably so—we humans have done some destructive things to the ecosystems around us. In the 21st century, though, mainstream conserv
arXiv:2510.03988v2 Announce Type: replace Abstract: Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argue
There's a broadly held misconception in AI that methods that scale well are simple methods -- even, that simple methods usually scale. This is completely wrong. Pretty much none of the truly simple me
arXiv:2604.13186v1 Announce Type: new Abstract: Unsafe surgical care is a critical health concern, often linked to limitations in surgeon experience, skills, and situational awareness. Integrating pat
arXiv:2604.14069v1 Announce Type: new Abstract: Human-Object Interaction (HOI) detection is a longstanding computer vision problem concerned with predicting the interaction between humans and objects.
arXiv:2604.14074v1 Announce Type: new Abstract: Semantic Multi-Object Tracking (SMOT) extends multi-object tracking with semantic outputs such as video summaries, instance-level captions, and interact
arXiv:2601.07422v2 Announce Type: replace Abstract: Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal stat
arXiv:2604.13565v1 Announce Type: new Abstract: Ultra-high-resolution (UHR) remote sensing imagery couples kilometer-scale context with query-critical evidence that may occupy only a few pixels. Such
arXiv:2604.13470v1 Announce Type: new Abstract: We prove that conditional diffusion models whose reverse kernels are finite Gaussian mixtures with ReLU-network logits can approximate suitably regular
arXiv:2604.13309v1 Announce Type: new Abstract: In this paper we present a novel visual servoing framework to control a robotic manipulator in the configuration space by using purely natural visual fe
arXiv:2604.13425v1 Announce Type: new Abstract: Video chroma-lux editing, which aims to modify illumination and color while preserving structural and temporal fidelity, remains a significant challenge
arXiv:2604.13687v1 Announce Type: cross Abstract: Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work in
Viktor Orbán’s electoral loss in Hungary is as much a defeat for Trump and JD Vance. 'Seldom have American leaders intervened so overtly in a foreign election, and seldom has their preferred candidate
arXiv:2506.01247v2 Announce Type: replace Abstract: Steering vision foundation models at test time, without updating foundation-model weights or using labeled target data, is a desirable yet challengi
arXiv:2604.02486v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal tasks. However, they often fail on tasks
arXiv:2604.13438v1 Announce Type: new Abstract: Privacy concerns in LLMs have led to the rapidly growing need to enforce a data's 'right to be forgotten'. Machine unlearning addresses precisely this t
arXiv:2604.13556v1 Announce Type: new Abstract: Cross-layer key-value (KV) compression has been found to be effective in efficient inference of large language models (LLMs). Although they reduce the m
arXiv:2511.05757v3 Announce Type: replace-cross Abstract: We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach i
arXiv:2604.12027v1 Announce Type: new Abstract: Recently, the robotics community has regained interest in radar-based perception and state estimation. A 2D imaging radar provides dense 360deg informat