Knowledge catalogue
arXiv:2606.30516v1 Announce Type: new Abstract: Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic
arXiv:2603.06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing be
arXiv:2602.13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating
arXiv:2606.29095v1 Announce Type: new Abstract: Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short cl
arXiv:2602.12957v3 Announce Type: replace Abstract: Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extractio
arXiv:2606.30322v1 Announce Type: new Abstract: We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-bas
arXiv:2603.10604v3 Announce Type: replace Abstract: Generative models are increasingly used in video game engines to enhance the photorealism of rendered images for visual synthetic data generation an
In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM
arXiv:2606.28604v1 Announce Type: new Abstract: Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vi
arXiv:2405.01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial. It pr
arXiv:2606.29028v1 Announce Type: new Abstract: Keypose-based manipulation decomposes tasks into critical waypoints to simplify policy learning for long-horizon tasks, but existing approaches rely on
arXiv:2601.21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing metho
arXiv:2606.29374v1 Announce Type: new Abstract: High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Ga
arXiv:2606.28841v1 Announce Type: cross Abstract: Large language models are increasingly capable of mathematical reasoning, but the proofs they generate are often unreliable and hard to verify. Intera
arXiv:2606.29928v1 Announce Type: cross Abstract: Multimodal Large Models have significantly advanced automated breast ultrasound diagnosis. However, most existing frameworks utilize opaque, end-to-en
arXiv:2405.13947v2 Announce Type: replace Abstract: Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown
arXiv:2606.29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled. This occurs when engines upscale frames, users zoom into 3D scenes,
arXiv:2606.28441v1 Announce Type: cross Abstract: Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for divers
arXiv:2602.02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling
arXiv:2606.30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to exe
arXiv:2606.29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of commu
arXiv:2606.28827v1 Announce Type: new Abstract: End-to-end unmanned aerial vehicle (UAV) navigation can achieve impressive agility in simulation, yet its obstacle-avoidance behavior often degrades aft
arXiv:2601.05366v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. While recent wo
arXiv:2606.29788v1 Announce Type: new Abstract: When a multimodal AI agent is asked to forget a fact, current memory systems usually delete the text entry and report success. We find that the fact can
arXiv:2606.29038v1 Announce Type: cross Abstract: Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi
arXiv:2606.29697v1 Announce Type: new Abstract: For uncrewed aerial vehicles (UAVs), estimating six-degree-of-freedom (6-DoF) poses is essential for airspace situational awareness, target tracking, an
arXiv:2606.29462v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) inherit rich relational priors from their language backbones, yet often fail when asked to apply these relation
arXiv:2606.30182v1 Announce Type: new Abstract: AI models are rapidly improving at autonomous coding, as shown by benchmark progress and one-off demonstrations such as AI implementing a C compiler. Ho
arXiv:2606.29308v1 Announce Type: new Abstract: While text-guided image editing has made remarkable progress, it remains limited in structural portrait retouching. Textual descriptions struggle to con
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:2606.29013v1 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:2606.29395v1 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
Vercel has announced the availability of Nano Banana 2 Lite, a lightweight image variant featuring Google's Gemini 3.1 Flash model, through its AI Gateway service. This release likely provides develop
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: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:2606.18066v2 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:2606.30378v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated promising spatial reasoning capabilities, while these abilities remain underexplored in the e
arXiv:2606.28795v1 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:2606.29600v1 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:2606.29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to
arXiv:2606.28396v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, envi
arXiv:2606.29855v1 Announce Type: new Abstract: In precipitation forecasting, not only accuracy but also temporal resolution is critical. However, increasing temporal resolution is constrained by obse
Ray Data 2.56 introduces reliability improvements for AI data pipelines, likely including enhancements to data processing, fault tolerance, and stability features within the Ray distributed computing
arXiv:2603.11404v3 Announce Type: replace-cross Abstract: Accurate and efficient tracking of surgical instruments is fundamental for Robot-Assisted Minimally Invasive Surgery. Although vision-based ro
arXiv:2606.30314v1 Announce Type: new Abstract: Real-time underwater image enhancement (UIE) is crucial for mobile underwater photography and autonomous robotic systems, where practical deployment typ
arXiv:2603.25743v2 Announce Type: replace Abstract: Reference-to-video (R2V) generation is a controllable video synthesis paradigm that constrains the generation process using both text prompts and re
arXiv:2606.29937v1 Announce Type: new Abstract: Understanding how users perceive and respond to robot failures is essential for building robust and trustworthy robot systems. Prior work, however, (i)
arXiv:2606.29538v1 Announce Type: cross Abstract: Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill librari
arXiv:2602.09305v2 Announce Type: replace Abstract: Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning (
arXiv:2606.29942v1 Announce Type: new Abstract: Anticipation of human behaviours facilitates autonomous systems in proactive planning. Human behaviour could be stochastic due to varying goals. Human g
arXiv:2606.29847v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) excel at multimodal tasks but remain prone to object hallucinations. Prior training-free remedies often uniformly s
arXiv:2606.29941v1 Announce Type: cross Abstract: Visuo-Tactile policies leveraging optical tactile sensors have shown great promise in contact-rich manipulation. These sensors achieve high spatial re
arXiv:2606.28747v1 Announce Type: new Abstract: Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning. Many existing approaches, including large language
arXiv:2606.30012v1 Announce Type: new Abstract: Volume microscopy, including electron and light microscopy, suffers from severe anisotropic resolution due to physical axial sectioning. Existing self-s
arXiv:2606.29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recent
arXiv:2602.15727v2 Announce Type: replace-cross Abstract: Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformat
arXiv:2602.12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-
arXiv:2606.30545v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view
arXiv:2506.20771v2 Announce Type: replace Abstract: We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamic