Knowledge catalogue
arXiv:2609.06058v1 Announce Type: new Abstract: As vision-language models (VLMs) become increasingly capable and are deployed in consequential real-world settings, they must evaluate evidence independ
arXiv:2512.15560v3 Announce Type: replace Abstract: The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the
arXiv:2609.06016v1 Announce Type: new Abstract: Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this s
arXiv:2609.08802v1 Announce Type: new Abstract: This paper presents a graph-based safe multi-agent reinforcement learning (MARL) framework for cooperative navigation with time-varying topology. To add
arXiv:2609.07456v1 Announce Type: cross Abstract: We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models. Efficiently finding these ground states is of
arXiv:2609.08970v1 Announce Type: new Abstract: Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not mee
arXiv:2609.05694v1 Announce Type: new Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to od
arXiv:2609.05880v1 Announce Type: new Abstract: Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what con
arXiv:2609.08449v1 Announce Type: new Abstract: Gaussian splats provide a fast, high-fidelity representation for 3D objects but are often constructed from incomplete input data in practice, leaving mi
arXiv:2609.07749v1 Announce Type: new Abstract: Crowdsourcing platforms coordinate large pools of online workers who strategically choose which contests to enter and how much effort to invest. This se
arXiv:2609.06796v1 Announce Type: cross Abstract: Multi-chiplet photonic neural network accelerators (MCPNAs) combine photonic computation, photonic communica-tion, and heterogeneous chiplet integrati
arXiv:2609.07670v1 Announce Type: new Abstract: The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfa
arXiv:2609.05582v1 Announce Type: new Abstract: Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label h
arXiv:2609.06976v1 Announce Type: new Abstract: As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients
arXiv:2609.07975v1 Announce Type: new Abstract: Bringing multiscale geometric analysis directly to irregular point clouds remains difficult: quantities such as local dimension, anisotropy, density var
arXiv:2609.07557v1 Announce Type: new Abstract: 3D Gaussian Splatting has recently revolutionised novel view synthesis as well as many other 3D vision methods and applications. Drawing inspiration fro
arXiv:2603.23997v3 Announce Type: replace Abstract: Recovering high-fidelity 3D hand geometry from images is a critical task in computer vision, holding significant value for domains such as robotics,
arXiv:2609.08796v1 Announce Type: new Abstract: Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop gen
arXiv:2609.08678v1 Announce Type: new Abstract: In multi-party human-robot interaction, a robot must continuously decide whom to address and what to say to participate effectively in the conversation.
arXiv:2609.06557v1 Announce Type: new Abstract: Large language models (LLMs) are often considered fragile under aggressive sparsification, and maintaining reliable performance typically requires stick
arXiv:2609.06307v1 Announce Type: cross Abstract: We study variational quantum distribution learning through a hierarchy of Walsh--Fourier approximations on the Boolean cube. At each level, a selected
arXiv:2609.05864v1 Announce Type: new Abstract: Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domai
arXiv:2311.01327v3 Announce Type: replace Abstract: We investigate the contextual bandits with knapsack (CBwK) problem in a high-dimensional linear setting, where the feature dimension can be very lar
arXiv:2609.08873v1 Announce Type: cross Abstract: Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the
arXiv:2609.08306v1 Announce Type: cross Abstract: We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated honeypo
arXiv:2609.08685v1 Announce Type: new Abstract: Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are often
arXiv:2609.06483v1 Announce Type: new Abstract: This study focuses on the scientific problem of understanding internal factors that govern the diverse performance degradation of deep neural networks (
arXiv:2609.07288v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly employed to orchestrate robot behavior through natural-language interfaces, yet no benchmark exists to eva
arXiv:2504.21323v3 Announce Type: replace-cross Abstract: Knowledge distillation is widely used to transfer behavior from a large teacher model to a smaller student. It is often assumed to be safe whe
arXiv:2609.06009v1 Announce Type: new Abstract: Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the pro
arXiv:2609.08380v1 Announce Type: cross Abstract: We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone varia
arXiv:2412.09529v4 Announce Type: replace Abstract: Radiology, with its heterogeneous modalities, anatomies, and evolving protocols, is a natural yet high-stakes testbed for agentic AI. As LLMs grow m
arXiv:2609.07091v1 Announce Type: new Abstract: Autonomous mobile robots performing person-following tasks often suffer from temporary occlusions and sensor track loss in dynamic environments. This re
arXiv:2609.08300v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural halluc
arXiv:2403.14293v3 Announce Type: replace Abstract: As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study syste
arXiv:2609.07990v1 Announce Type: new Abstract: Sequential reinforcement learning (RL) solvers and global diffusion model (DM) solvers for neural combinatorial optimization exhibit complementary failu
arXiv:2603.26517v2 Announce Type: replace-cross Abstract: The discovery of constitutive laws from experimentally accessible measurements is a central problem in nonlinear computational mechanics. Many
arXiv:2609.08690v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models expand model capacity without a proportional increase in training compute, but increasing sparsity makes reliable hyperp
arXiv:2601.21255v2 Announce Type: replace Abstract: A central problem in self-supervised learning is preventing representation collapse. Most methods avoid it through global mechanisms, such as contra
arXiv:2609.08121v1 Announce Type: new Abstract: Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computationa
arXiv:2603.25255v2 Announce Type: replace Abstract: Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidenc
arXiv:2609.07017v1 Announce Type: new Abstract: Hyperball optimizers constrain parameter norms and update only their directions, establishing a distinct paradigm for neural network optimization. Altho
arXiv:2609.08286v1 Announce Type: new Abstract: Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained lo
arXiv:2609.07596v1 Announce Type: cross Abstract: Vision-language models are increasingly used in settings where some input modalities may be unavailable, yet we know little about whether they can fai
arXiv:2609.07581v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are commonly adapted to new manipulation settings through additional gradient updates, which limits rapid deployme
arXiv:2505.21465v2 Announce Type: replace Abstract: Currently, a prevalent approach for enhancing Vision-Language Models (VLMs) performance is to encode both the high-resolution version and the thumbn
arXiv:2609.06131v1 Announce Type: cross Abstract: Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situation
arXiv:2609.06279v1 Announce Type: cross Abstract: Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often prov
arXiv:2609.06051v1 Announce Type: new Abstract: The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogene
arXiv:2508.06990v2 Announce Type: replace Abstract: Semantic navigation requires an agent to navigate toward a specified target in an unseen environment. Employing an imaginative navigation strategy t
arXiv:2609.07408v1 Announce Type: cross Abstract: This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on
arXiv:2602.04761v2 Announce Type: replace Abstract: Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory and optimization. It has been studied e
arXiv:2606.15277v2 Announce Type: replace-cross Abstract: Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning
arXiv:2509.19994v3 Announce Type: replace Abstract: Multimodal pre-trained models (e.g., ImageBind), which align distinct data modalities into a shared embedding space, have shown remarkable success a
arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying
arXiv:2609.08463v1 Announce Type: cross Abstract: We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections. Starting from an ensemble of deep neural
arXiv:2609.06649v1 Announce Type: new Abstract: Reward hacking during reinforcement learning from verifiable rewards (RLVR) can induce reward seeking and broad misalignment in language models. Studyin
arXiv:2609.07349v1 Announce Type: new Abstract: Real-time urban governance depends not only on knowing where people are, but on how they move between places, directional flows that could be convention
arXiv:2609.07874v1 Announce Type: new Abstract: Multimodal large language models often capture visual-linguistic correlations but struggle to predict how local visual interventions propagate and affec
arXiv:2609.05569v1 Announce Type: new Abstract: The autonomous localization of fugitive gas emissions using small Unmanned Aircraft Systems (sUAS) constitutes a fundamentally ill-posed inverse problem