Knowledge catalogue
arXiv:2608.06396v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns useful for identifying task-relev
arXiv:2608.06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applic
arXiv:2608.07274v1 Announce Type: cross Abstract: Split Federated Learning (SFL) facilitates privacy-preserving collaborative training with reduced client-side overhead. However, its split architectur
arXiv:2608.06394v1 Announce Type: new Abstract: Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing
arXiv:2608.07015v1 Announce Type: new Abstract: Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains an
arXiv:2608.06809v1 Announce Type: new Abstract: How can an analyst decide whether a nonlinear dimensionality reduction embedding can be trusted? Existing diagnostics provide only partial answers: proj
arXiv:2608.06784v1 Announce Type: new Abstract: Bidirectional unpaired image translation must preserve source-specific structure while learning coherent transformations in both directions without pair
arXiv:2608.07053v1 Announce Type: new Abstract: Pretrained partial differential equation (PDE) foundation models can generalize across different equations, but adapting them to unseen PDE systems typi
arXiv:2608.06919v1 Announce Type: new Abstract: LiDAR serves as a primary sensing modality for robots operating in outdoor environments. However, the performance of deep learning models in this domain
arXiv:2608.06934v1 Announce Type: cross Abstract: Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and prefere
arXiv:2608.06823v1 Announce Type: cross Abstract: In this paper, we extend a weak adversary neural network pushforward method for solving time dependent Boltzmann equation and a weak formulation of th
arXiv:2608.06973v1 Announce Type: new Abstract: Aerial drone surveys increasingly support wildlife population estimation, yet a useful census is more than a count: population dynamics are defined by s
arXiv:2608.06673v1 Announce Type: new Abstract: Language descriptions in source-free cross-domain few-shot learning (SF-CDFSL) are often selected according to zero-shot accuracy obtained with a frozen
arXiv:2608.05471v1 Announce Type: cross Abstract: Prenatal ultrasound imaging is key for assessing fetal health, but AI progress is limited by scarce, privacy-restricted, and hard-to-annotate datasets
arXiv:2608.05697v1 Announce Type: cross Abstract: Respiration provides a continuously available window into physiological state and behavior. However, monitoring it outside controlled settings remains
arXiv:2608.06354v1 Announce Type: new Abstract: We present a MR safe, master-slave robot manipulator for abdominal interventions in the MRI chamber. A human operated 2+1-DoF master controller manipula
arXiv:2608.05570v1 Announce Type: cross Abstract: 360-degree video telepresence offers strong immersive potential but remains constrained by the limited resolution of current capture and display hardw
arXiv:2608.05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few work
arXiv:2512.03057v2 Announce Type: replace-cross Abstract: We study distribution-free risk control for model routing, motivated by large language model reasoning. We formalize pointwise conditional eff
arXiv:2505.06800v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have renewed interest in stochastic differential equation methods for sampling from complex distributions. W
arXiv:2608.05165v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) in low-resource languages remains a challenging problem due to limited labeled data. In this work, we study the use o
arXiv:2608.05217v1 Announce Type: cross Abstract: Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets.
arXiv:2608.05673v1 Announce Type: new Abstract: Trajectory prediction has shifted toward structured formulations with explicit social modeling. However, existing methods inadequately distinguish the f
arXiv:2608.05737v1 Announce Type: cross Abstract: Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing
arXiv:2509.16684v2 Announce Type: replace Abstract: Multi-view crowd counting and localization fuse the input multi-views for estimating the crowd number or locations on the ground. Existing methods m
arXiv:2509.10482v2 Announce Type: replace-cross Abstract: The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with l
arXiv:2608.05502v1 Announce Type: cross Abstract: In this paper, we consider a class of multiblock nonconvex nonsmooth optimization problems, which covers many applications such as the analysis of pre
arXiv:2608.06363v1 Announce Type: cross Abstract: Let Hsubseteq{-1,+1}^X be a class of finite VC dimension dge1. Writing L for the binary risk and L^*=min_{hin H}L(h), we construct a learner achieving
arXiv:2608.05687v1 Announce Type: cross Abstract: Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding. We
arXiv:2608.05183v1 Announce Type: cross Abstract: This dissertation analysed and discussed the differences in linguistic characteristics between pre-mortem and post-mortem social media content, and re
arXiv:2608.06291v1 Announce Type: cross Abstract: We accelerate a family of algorithms for neural network quantization whose geometry is informed by any Kronecker-factored approximation of the Hessian
arXiv:2608.05926v1 Announce Type: cross Abstract: Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference main
arXiv:2608.05815v1 Announce Type: new Abstract: Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning al
arXiv:2608.05642v1 Announce Type: new Abstract: This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its exp
arXiv:2608.05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message aut
arXiv:2608.06164v1 Announce Type: new Abstract: Reconstructing objects with mechanical properties from video observations enables physically consistent dynamic prediction, benefiting robotics planning
arXiv:2607.16465v2 Announce Type: replace Abstract: Edmund C. Berkeley is usually remembered as a mediator between symbolic logic and early computing, yet that standard description understates the sco
arXiv:2608.05999v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated remarkable capabilities in robotic manipulation by leveraging pretrained vision-language models. H
arXiv:2608.05592v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong progress in video understanding, yet it remains challenging because the token limitation m
arXiv:2608.05171v1 Announce Type: cross Abstract: Generative AI (GAI) creates new opportunities for collaborative problem-solving (CPS), yet its role in shaping student interaction remains unclear. To
🚨 BREAKING — Anthropic investors worry Dario Amodei’s AI doom marketing could hurt its upcoming IPO. “He’s more of a religious leader than he is a CEO.” > used to write sensitive OpenAI memos on an of
arXiv:2608.05482v1 Announce Type: new Abstract: Modern image models provide strong cues about what should be segmented in each view, but their masks do not by themselves determine where those labels s
arXiv:2608.06205v1 Announce Type: new Abstract: RGB--T object detection exploits the complementary strengths of visible and infrared imagery, supporting robust perception in low-light, adverse-weather
arXiv:2603.08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integra
arXiv:2608.05233v1 Announce Type: new Abstract: Dreams can be emotionally intense but difficult to communicate. We describe the Dream Scene Visualiser (DSV) system which turns written dream descriptio
arXiv:2608.05900v1 Announce Type: new Abstract: Many single-cell annotation tools refine an initial cell label using nearby cells or cluster-level voting. We study whether this refinement can be manip
arXiv:2608.06117v1 Announce Type: new Abstract: 3D Gaussian splatting (3DGS) has emerged as a widely-used tool for novel view synthesis, offering real-time rendering in a sparse representation. Howeve
arXiv:2608.05675v1 Announce Type: new Abstract: Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a kn
arXiv:2608.05254v1 Announce Type: new Abstract: Large language models can derive a plausible mathematical object yet still violate explicit requirements--for example, by omitting a modular reduction,
arXiv:2608.05333v1 Announce Type: new Abstract: In-context learning (ICL) adapts medical image segmentation models to unseen structures and modalities without retraining by conditioning on a task-spec
arXiv:2608.05834v1 Announce Type: new Abstract: In this technical report, I present a new method for guiding image generation in the context of Virtual- Try-On (VITON). The proposed method leverages n
arXiv:2608.05830v1 Announce Type: new Abstract: Multi-robot task and motion planning for disassembly tasks requires robots to operate in confined workspaces while coordinating their motions with other
arXiv:2608.05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper inve
arXiv:2507.14022v2 Announce Type: replace Abstract: This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The
arXiv:2601.07568v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-orde
arXiv:2608.05448v1 Announce Type: new Abstract: Speculative decoding accelerates large language models' inference by using a lightweight drafter to propose multiple future tokens and a target model to
arXiv:2608.05823v1 Announce Type: new Abstract: The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated conten
arXiv:2608.06082v1 Announce Type: new Abstract: Proper short-term forecasting of precipitation is crucial in disaster management and preparedness. Nonetheless, the variability and nonlinearity of prec
arXiv:2608.05976v1 Announce Type: new Abstract: Recently, diffusion models have made great progress in video generation. However, most existing video diffusion models are trained with short videos, an
arXiv:2608.05437v1 Announce Type: cross Abstract: Supervised training of finite-element (FE) surrogate models requires reference solutions, and each reference solution is obtained by solving the syste