Knowledge catalogue
arXiv:2607.13415v1 Announce Type: new Abstract: Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation.
arXiv:2607.13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati
arXiv:2503.24092v2 Announce Type: replace-cross Abstract: Motivated by the rapidly growing field of mathematics for operator approximation with neural networks, we present a novel universal operator a
arXiv:2607.13704v1 Announce Type: new Abstract: The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based pla
arXiv:2607.13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction
arXiv:2607.13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventio
arXiv:2607.13104v1 Announce Type: new Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, fro
arXiv:2607.13192v1 Announce Type: new Abstract: Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approa
arXiv:2607.13693v1 Announce Type: cross Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly define
arXiv:2607.13539v1 Announce Type: new Abstract: While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based genera
arXiv:2607.13722v1 Announce Type: cross Abstract: The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adver
arXiv:2602.19108v2 Announce Type: replace Abstract: Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response. In this wor
arXiv:2607.13162v1 Announce Type: cross Abstract: What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or resists is not revealed by
arXiv:2607.13046v1 Announce Type: new Abstract: We develop a framework for the information discarded by machine learning models whose inputs carry a Lie group action. Given a representation pi of a Li
arXiv:2607.12293v1 Announce Type: new Abstract: Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or
In this post, we walk you through the Computer Vision MCP Server, which illustrates this approach, representing how AI systems can process visual information and make intelligent decisions through a s
arXiv:2607.12833v1 Announce Type: cross Abstract: Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regres
LLMs handle speech well once you run speech-to-text. They don't hear the rest: a bird outside, a glass breaking two rooms away, a smoke alarm two floors down. I've been working on an experimental open
arXiv:2607.11943v1 Announce Type: cross Abstract: Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for
arXiv:2607.12723v1 Announce Type: cross Abstract: Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities. T
arXiv:2607.11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neur
arXiv:2607.12165v1 Announce Type: new Abstract: Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of t
arXiv:2607.12570v1 Announce Type: cross Abstract: Multiscale problems are notoriously difficult to tackle using traditional numerical methods, as accurately resolving fine-scale features often require
arXiv:2509.21842v2 Announce Type: replace Abstract: Travel planning (TP) agent has recently worked as an emerging building block to interact with external tools/resources for travel itinerary generati
arXiv:2607.12419v1 Announce Type: new Abstract: In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current m
arXiv:2508.05659v4 Announce Type: replace Abstract: Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying
arXiv:2507.21245v2 Announce Type: replace-cross Abstract: An accurate initial heading angle is essential for efficient and safe navigation across diverse domains. Unlike magnetometers, gyroscopes can
arXiv:2509.24372v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has e
arXiv:2501.05396v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in high-stakes decisions such as hiring and college admissions, making their social bias a critic
arXiv:2505.16157v2 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Trans
arXiv:2512.01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety
arXiv:2607.12175v1 Announce Type: cross Abstract: X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric d
arXiv:2606.20751v2 Announce Type: replace Abstract: Advanced Air Mobility (AAM) is an emerging low-altitude transportation system whose successful deployment depends on both technological progress and
arXiv:2512.09925v2 Announce Type: replace Abstract: Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light trans
arXiv:2607.12626v1 Announce Type: cross Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based pol
arXiv:2607.12823v1 Announce Type: new Abstract: Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a
arXiv:2411.09484v5 Announce Type: replace Abstract: This paper provides a consistent and extensive evaluation of state-of-the-art filtering and refinement methods on common image matching pipelines. U
arXiv:2607.12406v1 Announce Type: new Abstract: The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model. Consequentl
arXiv:2607.12922v1 Announce Type: cross Abstract: Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box infor
arXiv:2604.04241v2 Announce Type: replace Abstract: Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predic
arXiv:2607.12775v1 Announce Type: cross Abstract: Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing
arXiv:2607.12713v1 Announce Type: cross Abstract: Generated tokens are a direct driver of the cost, latency, and energy of generative AI (GAI) code editing. We show the format of feedback is a lever o
arXiv:2510.18939v2 Announce Type: replace Abstract: Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling
arXiv:2607.11980v1 Announce Type: new Abstract: Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field's modeling knowledge is sca
arXiv:2607.12409v1 Announce Type: new Abstract: Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction
arXiv:2607.11937v1 Announce Type: new Abstract: Mirror Theory proposes that an intelligent system should be studied not only by what it represents, but by what coherent continuations it can sustain un
arXiv:2607.12297v1 Announce Type: new Abstract: The recent large video foundation model, SAM2, enables segment anything in both images and videos, serving as a powerful base model for various applicat
arXiv:2607.12186v1 Announce Type: new Abstract: In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capability.
arXiv:2607.13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and it
arXiv:2607.12385v1 Announce Type: new Abstract: A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activitie
arXiv:1809.01898v2 Announce Type: replace-cross Abstract: The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learnin
arXiv:2607.11916v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in com
arXiv:2508.19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i
arXiv:2503.06796v2 Announce Type: replace Abstract: Robot design is a complex and time-consuming process that requires specialized expertise. Gaining a deeper understanding of robot design data can en
arXiv:2607.12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic
arXiv:2607.11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromis
arXiv:2607.11975v1 Announce Type: cross Abstract: Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the
arXiv:2607.12866v1 Announce Type: new Abstract: Reconstruction-based methods are a cornerstone of unsupervised image anomaly detection, but they remain vulnerable to outlier leakage, where standard me
arXiv:2506.21324v3 Announce Type: replace-cross Abstract: Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complemen
arXiv:2607.12161v1 Announce Type: new Abstract: Context-reduction layers for API-based coding agents, including command-output compressors, retrieval rankers, and payload-optimizing proxies, are usual