Knowledge catalogue
arXiv:2607.03899v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specifi
arXiv:2607.05064v1 Announce Type: new Abstract: Reinforcement learning in real world environments often suffers from severe performance degradation due to delayed feedback. Existing approaches typical
arXiv:2607.03998v1 Announce Type: new Abstract: The local sharpness of the loss, the top Hessian eigenvalue lambda_1, determines the largest stable gradient step, but measuring it normally requires La
arXiv:2607.04599v1 Announce Type: new Abstract: Accurate 3D medical segmentation is limited by anatomical variability and high computational costs. While knowledge distillation (KD) offers a route for
arXiv:2607.02966v1 Announce Type: new Abstract: Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved
arXiv:2303.08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theo
arXiv:2508.19573v2 Announce Type: replace Abstract: Anomaly detection in medical images is challenging due to limited annotations and the domain gap. Existing reconstruction-based methods often rely o
arXiv:2607.03009v1 Announce Type: new Abstract: Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial inte
arXiv:2607.04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Acti
arXiv:2607.04428v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel alternative to autoregressive mo
arXiv:2607.02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
Drone delivery is about to be cheaper than cars. But the drone is only 15% of the work to get there. A decade after starting in Rwanda flying blood to one hospital, @zipline has flown 140M+ autonomous
arXiv:2607.04927v1 Announce Type: cross Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense super
arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene on
arXiv:2508.16170v2 Announce Type: replace-cross Abstract: MultiModal Recommendation (MMR) systems have emerged as a promising solution for improving recommendation quality by leveraging rich item-side
arXiv:2506.09740v2 Announce Type: replace-cross Abstract: Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode
arXiv:2607.04490v1 Announce Type: new Abstract: Facial expression recognition (FER) is crucial for social interaction in mixed reality environments that employ head-mounted displays (HMD). However, co
arXiv:2508.09629v2 Announce Type: replace Abstract: We revisit the role of texture in monocular 3D hand reconstruction, not as an afterthought for photorealism, but as a dense, spatially grounded cue
arXiv:2607.04862v1 Announce Type: new Abstract: Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due
arXiv:2506.05716v2 Announce Type: replace-cross Abstract: Deep Q-Networks (DQN) can suffer from overestimation bias because bootstrapped targets use a maximisation operation over noisy value estimates
arXiv:2607.03168v1 Announce Type: cross Abstract: Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its rob
arXiv:2607.04416v1 Announce Type: new Abstract: Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have pa
arXiv:2607.02646v1 Announce Type: cross Abstract: We present EVA-Client, an open-source framework for deployment, data collection, and evaluation of trained manipulation policies on real robots. Sitti
arXiv:2503.02368v4 Announce Type: replace-cross Abstract: While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs wit
arXiv:2607.03146v1 Announce Type: new Abstract: Vision-language-action (VLA) models open a new path toward intuitive robot control by directly linking perception, language, and action in a single end-
arXiv:2607.03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traf
arXiv:2508.04928v5 Announce Type: replace-cross Abstract: We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images. Despite being
arXiv:2509.15206v3 Announce Type: replace Abstract: The high memory demands of generative language models have drawn attention to quantization, which reduces memory usage by mapping model weights to l
arXiv:2607.05355v1 Announce Type: new Abstract: Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing fo
arXiv:2510.15686v2 Announce Type: replace Abstract: Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial tr
arXiv:2607.04019v1 Announce Type: cross Abstract: Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use. We introduce Finite Re
arXiv:2607.04006v1 Announce Type: cross Abstract: We establish finite-sample closed-loop stability guarantees for Model Predictive Path Integral (MPPI) control applied to discrete-time Linear Time-Inv
arXiv:2607.05375v1 Announce Type: cross Abstract: Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minim
arXiv:2607.03509v1 Announce Type: new Abstract: Recent progress in large-scale generative models has substantially advanced video generation, yet existing methods remain constrained by a rigid inferen
arXiv:2607.04260v1 Announce Type: new Abstract: Aerial physical interaction represents a promising direction for next-generation unmanned aerial vehicles (UAVs), but it requires an aerial platform tha
arXiv:2607.04750v1 Announce Type: new Abstract: We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature
arXiv:2607.04125v1 Announce Type: new Abstract: Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illuminatio
arXiv:2607.05396v1 Announce Type: cross Abstract: Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on
arXiv:2607.04691v1 Announce Type: new Abstract: While controllable image generation has made significant strides by incorporating visual reference conditions, existing methods predominantly operate as
arXiv:2607.03792v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) agents may satisfy conventional success criteria while still failing to establish reliable object-level grounding
Full clip of my presentation of the Independent International Scientific Panel’s preliminary report at the opening ceremony yesterday: https://webtv.un.org/en/asset/k1y/k1yd3dlebq?kalturaSeekFrom=3260
arXiv:2607.04422v1 Announce Type: cross Abstract: Recent NVFP4 pretraining methods mainly target transformer linear layers, leaving optimizer states, optimizer arithmetic and attention underexplored i
arXiv:2607.04607v1 Announce Type: cross Abstract: The rapid advancement of AI-generated videos poses increasing security risks and calls for robust detectors with strong cross-domain generalization. A
arXiv:2607.05369v1 Announce Type: cross Abstract: For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot progr
arXiv:2602.20995v2 Announce Type: replace-cross Abstract: Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstre
arXiv:2607.04103v1 Announce Type: cross Abstract: The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more risk-based and materiality-sen
arXiv:2602.18037v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Model
arXiv:2607.02592v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm by providing supervision on student-generated trajectories. How
arXiv:2607.04265v1 Announce Type: cross Abstract: World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration,
arXiv:2607.04243v1 Announce Type: new Abstract: Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-
arXiv:2607.03865v1 Announce Type: cross Abstract: Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their
arXiv:2607.03512v1 Announce Type: new Abstract: Existing classical control methods commonly require precise models and struggle to cope with model uncertainties and external disturbances, while end-to
arXiv:2607.03155v1 Announce Type: new Abstract: The deployment of autonomous vehicles in urban environments introduces significant safety challenges, particularly in scenarios with occlusions, where c
arXiv:2607.03561v1 Announce Type: new Abstract: As AI models continue to develop powerful capabilities, it becomes critical that we are able to verify that their output is aligned with our intentions.
arXiv:2603.22730v2 Announce Type: replace Abstract: Pfeffer, Krugel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbrid
arXiv:2607.03025v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhan
arXiv:2510.08807v3 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities. Howev
arXiv:2606.12471v2 Announce Type: replace-cross Abstract: Klindt, LeCun, and Balestriero (arXiv:2605.26379) proved that Joint-Embedding Predictive Architectures (JEPAs) achieve linear identifiability,
arXiv:2607.03613v1 Announce Type: new Abstract: We study the implicit bias of noisy stochastic gradient descent in training wide two-layer ReLU networks for multivariate regression. In a mean-field re
arXiv:2607.02668v1 Announce Type: new Abstract: Large language models are inconsistent: varying prompts or including unrelated information can lead to unexpected changes in model outputs. The generato