Knowledge catalogue
arXiv:2603.02043v2 Announce Type: replace Abstract: We revisit transductive learning where predictions are made with the set of all covariates known in advance. In the leave-one-out (LOO) setting, the
arXiv:2105.09254v4 Announce Type: replace-cross Abstract: In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interes
arXiv:2606.30509v1 Announce Type: new Abstract: Matrix factorization (i.e., problems of the form min_{mathbf{P},mathbf{Q}} |mathbf{M}^star - mathbf{P}^opmathbf{Q}|_F^2) is a minimal learning problem t
arXiv:2606.30461v1 Announce Type: new Abstract: State space models (SSMs) have emerged as efficient linear-time alternatives to attention for long-sequence modeling. However, existing SSMs often suffe
arXiv:2606.29013v1 Announce Type: new Abstract: Leveraging capabilities of large language models (LLMs) in text-to-image (T2I) synthesis is an important research direction. In this work we investigate
arXiv:2606.30370v1 Announce Type: new Abstract: Monocular dense prediction has recently seen remarkable success by repurposing pre-trained diffusion models. This opens a promising yet challenging aven
arXiv:2606.30026v1 Announce Type: cross Abstract: Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning ari
arXiv:2606.29738v1 Announce Type: new Abstract: Real-time monocular Simultaneous Localization and Mapping (SLAM) fundamentally suffers from scale ambiguity and a lack of geometric self-correction. Whi
arXiv:2606.29395v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have emerged as promising layout agents for 3D scene generation. Existing layout agents still suffer from implaus
arXiv:2505.01423v2 Announce Type: replace-cross Abstract: Efficient computation of min-max problems is a central question in optimization, learning, games, and control. Arguably the most natural algor
arXiv:2606.29814v1 Announce Type: new Abstract: We propose Nemotron-Labs-Diffusion-Image, a state-of-the-art masked discrete diffusion model (MDM) for high-resolution text-to-image synthesis. Compared
arXiv:2603.18907v2 Announce Type: replace Abstract: We propose a new Neural Galerkin Normalizing Flow framework to approximate the transition probability density function of a diffusion process by sol
arXiv:2603.12598v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have shown remarkable potential across a wide array of vision-language tasks, leading to their adoption in crit
arXiv:2601.00242v2 Announce Type: replace-cross Abstract: Realizing the full potential of quantum computation requires Quantum Error Correction (QEC). QEC reduces error rates by encoding logical infor
arXiv:2606.29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires con
arXiv:2504.20383v3 Announce Type: replace Abstract: Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can
arXiv:2606.30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces. Instead of treating
arXiv:2606.18664v2 Announce Type: replace-cross Abstract: Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effective
arXiv:2606.29971v1 Announce Type: new Abstract: A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training prima
arXiv:2606.28600v1 Announce Type: cross Abstract: Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems
arXiv:2505.12526v2 Announce Type: replace Abstract: Temporal graph networks suffer from irregular supervision in realworld dynamic graphs, as most minibatches contain few labeled events. The lack of l
arXiv:2404.05062v4 Announce Type: replace-cross Abstract: We present four new mathematical methods, two exact and two approximate, along with open-source software, to compute the cdf, pdf and inverse
arXiv:2606.28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost. However, existing data-driven
arXiv:2606.30009v1 Announce Type: new Abstract: Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Ex
arXiv:2606.18066v2 Announce Type: replace Abstract: We introduce the Noise-Tilted Reverse Kernel (NTRK), a reward-guided diffusion sampler that injects reward gradients through the noise term, leaving
arXiv:2606.30467v1 Announce Type: cross Abstract: We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover t
arXiv:2606.29261v1 Announce Type: cross Abstract: We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separatio
arXiv:2606.29868v1 Announce Type: new Abstract: Accurate, robust, and adaptive localization is essential for various robotic operations. This paper proposes a new message passing (MP) algorithm for re
arXiv:2606.29521v1 Announce Type: new Abstract: Deep learning problems rarely involve objectives that are equal in importance. A primary objective defines the goal, whilst secondary objectives, such a
arXiv:2602.21160v4 Announce Type: replace-cross Abstract: In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a
arXiv:2606.30085v1 Announce Type: new Abstract: Large-language models have proven to be remarkable if inconsistent parrots of public attitudes and opinions. The extent to which LLMs are able to produc
arXiv:2606.30053v1 Announce Type: cross Abstract: These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between op
arXiv:2606.28643v1 Announce Type: new Abstract: The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitiga
arXiv:2606.29303v1 Announce Type: new Abstract: We propose a mask-free method for lossless multi-object 3D reconstruction from sparse and occluded real-world views, enabling physically plausible inter
arXiv:2601.22823v2 Announce Type: replace-cross Abstract: We study offline reinforcement learning of style-conditioned policies using explicit style supervision via subtrajectory labeling functions. I
arXiv:2405.10703v4 Announce Type: replace Abstract: Safe control in unknown environments is a key challenge in mobile robotics. Control Barrier Functions (CBFs) provide a principled framework for guar
arXiv:2606.30356v1 Announce Type: new Abstract: We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that joi
arXiv:2606.30378v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated promising spatial reasoning capabilities, while these abilities remain underexplored in the e
arXiv:2606.30019v1 Announce Type: new Abstract: Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidel
arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive perf
arXiv:2606.19147v2 Announce Type: replace-cross Abstract: We develop finite-sample certificates for local population-risk increments (Pelta_v=R(heta_0+v)-R(heta_0)), (vinmathcal D). The primitive obje
arXiv:2606.28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strengt
arXiv:2606.28864v1 Announce Type: new Abstract: Test-time scaling is a paradigm where large models use additional compute at inference to achieve better performance, without changing model weights. Wh
arXiv:2606.30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color. To bridge the gap between opaqu
arXiv:2606.29123v1 Announce Type: new Abstract: In aided inertial navigation, measurements from different sensors are often subject to unknown relative time delays. Consider a single aiding sensor who
arXiv:2606.28413v1 Announce Type: cross Abstract: A mesh of sovereign agents has no center: no shared clock, no shared model, and no coordinator to gather data or retrain. Its competence rests on each
arXiv:2606.29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrap
arXiv:2606.29238v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a
arXiv:2606.30360v1 Announce Type: cross Abstract: The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized m
arXiv:2606.30084v1 Announce Type: new Abstract: MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong
arXiv:2606.29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid
arXiv:2606.30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bu
arXiv:2606.30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged a
arXiv:2603.16856v2 Announce Type: replace Abstract: The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the
arXiv:2502.11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering
arXiv:2606.29786v1 Announce Type: new Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments. Although advances in foundation models have enabled open-vocabu
arXiv:2606.28867v1 Announce Type: new Abstract: Creative Commons licenses dominate African NLP corpus releases, but their compatibility rules are rarely applied. CC-BY-SA and CC-BY-NC cannot be combin
arXiv:2606.30116v1 Announce Type: new Abstract: Pairwise preference data is widely used for training and evaluating language models (e.g., RLHF), but each datapoint records a choice, not the rationale
arXiv:2606.30638v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged at the forefront of 3D scene reconstruction. Extending 3DGS with language-driven, open-vocabulary understanding
arXiv:2606.29936v1 Announce Type: new Abstract: Open-environment tabletop robotic manipulation requires systems to possess semantic understanding, precise geometric pose estimation, and high-frequency