Knowledge catalogue
arXiv:2604.22787v1 Announce Type: cross Abstract: Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion
arXiv:2604.23563v1 Announce Type: cross Abstract: Privacy-critical domains require phishing detection systems that satisfy contradictory constraints: near-zero false positives to prevent workflow disr
arXiv:2604.14989v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area
arXiv:2604.23730v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capaci
arXiv:2604.23990v1 Announce Type: new Abstract: This paper presents PSA-Eval, a failure-centered runtime evaluation framework for deployed trilingual public-space agents. The central claim is that, wh
arXiv:2604.23002v1 Announce Type: new Abstract: Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields suc
arXiv:2604.23335v1 Announce Type: new Abstract: Knee osteoarthritis (KOA) is a degenerative joint disease that can lead to chronic pain, reduced mobility, and long-term disability. Automated severity
arXiv:2604.23632v1 Announce Type: new Abstract: Real-time text-driven joint audio-video avatar generation requires jointly synthesizing portrait video and speech with high fidelity and precise synchro
arXiv:2604.23716v1 Announce Type: new Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross
arXiv:2604.23500v1 Announce Type: cross Abstract: Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, li
arXiv:2604.23493v1 Announce Type: cross Abstract: Early detection of mental health conditions, particularly stress and depression, from social media text remains a challenging open problem in computat
arXiv:2604.22795v1 Announce Type: cross Abstract: This study presents a multi-agent reinforcement learning (MARL) framework for load-constrained wind farm flow control (WFFC). While wake steering can
arXiv:2508.05318v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Mo
arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment acti
arXiv:2603.18514v2 Announce Type: replace-cross Abstract: Motivated by the principle of satisficing in decision-making, we study satisficing regret guarantees for nonstationary K-armed bandits. We sho
arXiv:2602.19035v2 Announce Type: replace Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectivel
arXiv:2604.23692v1 Announce Type: cross Abstract: Audio-driven facial animation is essential for immersive digital interaction, yet existing frameworks fail to reconcile real-time streaming with high-
arXiv:2503.05587v3 Announce Type: replace-cross Abstract: Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustne
arXiv:2510.11541v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge so
arXiv:2604.23442v1 Announce Type: new Abstract: Weeds compete with crops for light, water, and nutrients, reducing yield and crop quality. Efficient weed detection is essential for site-specific weed
arXiv:2604.22805v1 Announce Type: cross Abstract: Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic
arXiv:2604.24199v1 Announce Type: cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Ra
arXiv:2604.22843v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be fact
arXiv:2604.24155v1 Announce Type: cross Abstract: The quest to align machine behavior with human values raises fundamental questions about the moral frameworks that should govern AI decision-making. M
arXiv:2604.24235v1 Announce Type: new Abstract: Touchless interaction with medical images is becoming increasingly important in the surgical field, where sterility and continuity of the operational wo
arXiv:2604.22824v1 Announce Type: cross Abstract: WeatherSeg, an advanced semi-supervised segmentation framework, addresses autonomous driving's environmental perception challenges in adverse weather
arXiv:2601.10038v2 Announce Type: replace-cross Abstract: Artificial intelligence is rapidly transforming astronomical research, yet the scientific community has largely treated this transformation as
arXiv:2511.14135v2 Announce Type: replace-cross Abstract: Fair workload enforcement in heterogeneous multi-agent systems that pursue shared objectives remains challenging. Fixed fairness penalties oft
arXiv:2604.22306v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dial
arXiv:2602.05639v4 Announce Type: replace Abstract: We introduce Variational Joint Embedding (VJE), a reconstruction-free latent-variable framework for non-contrastive self-supervised learning in repr
arXiv:2604.22518v1 Announce Type: new Abstract: We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets c
arXiv:2604.22240v1 Announce Type: new Abstract: Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on
arXiv:2604.22491v1 Announce Type: cross Abstract: Selecting out-of-reach objects is a fundamental task in mixed reality (MR). Existing methods rely on a single cue or deterministically fuse multiple c
arXiv:2603.19500v2 Announce Type: replace Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel mu
arXiv:2604.22177v1 Announce Type: new Abstract: Multimodal MRI offers complementary information for brain tumor segmentation, but clinical scans often lack one or more modalities, which degrades segme
arXiv:2604.21432v1 Announce Type: cross Abstract: In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over tim
arXiv:2603.01170v2 Announce Type: replace-cross Abstract: This work presents ATLAS, an LLM-driven framework that bridges standardized threat modeling and property-based formal verification for System-
arXiv:2512.06171v2 Announce Type: replace Abstract: Shearography is an interferometric technique sensitive to surface displacement gradients, providing high sensitivity for detecting subsurface defect
arXiv:2604.20924v1 Announce Type: new Abstract: Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration.
arXiv:2604.21068v1 Announce Type: cross Abstract: The most technologically consequential materials are often the rarest: they occupy narrow regions of chemical space, obey competing physical constrain
arXiv:2604.21073v1 Announce Type: cross Abstract: Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, p
arXiv:2604.21175v1 Announce Type: new Abstract: We propose a learning-augmented framework for accelerating max-flow computation and image segmentation by integrating Graph Neural Networks (GNNs) with
SkyRL is a reinforcement learning framework from Anyscale that integrates vision-language models to enable agents to learn from visual observations and natural language instructions. The approach comb
arXiv:2603.28342v2 Announce Type: replace Abstract: We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary a
arXiv:2604.20886v1 Announce Type: cross Abstract: We present KinetiDiff, a structure-based framework for de novo kinase inhibitor design that integrates a Geometry-Complete Diffusion Model with real-t
arXiv:2604.21102v1 Announce Type: cross Abstract: We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLM
arXiv:2602.01493v2 Announce Type: replace-cross Abstract: Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrate
arXiv:2603.23547v2 Announce Type: replace-cross Abstract: Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures
arXiv:2604.21894v1 Announce Type: new Abstract: Designing multi-agent robotic systems requires reasoning across tightly coupled decisions spanning heterogeneous domains, including robot design, fleet
arXiv:2604.21263v1 Announce Type: new Abstract: extbf{Background:} Regulatory frameworks for AI in healthcare, including the EU AI Act and FDA guidance on AI/ML-based medical devices, require clinical
arXiv:2604.04685v2 Announce Type: replace-cross Abstract: We propose a data-adaptive probabilistic intensity remapping framework for structure-preserving transformation of grayscale images. The sugges
arXiv:2604.20372v1 Announce Type: cross Abstract: We report the first systematic evidence of hallucination in AI models of fluid dynamics, demonstrated in the canonical problem of hydrodynamically uns
arXiv:2604.20166v1 Announce Type: new Abstract: Building trustworthy AI systems for mental health support is a shared priority across stakeholders from multiple disciplines. However, 'trustworthy' rem
arXiv:2604.20070v1 Announce Type: cross Abstract: Advances in AI agent capabilities have outpaced users' ability to meaningfully oversee their execution. AI agents can perform sophisticated, multi-ste
arXiv:2604.19785v1 Announce Type: cross Abstract: Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messag
LangChain, an open-source framework for developing applications with large language models, is recruiting new team members as shared by co-founder Harrison Chase on X (Twitter). The careers page invit
arXiv:2604.19926v1 Announce Type: new Abstract: Large language models can generate plausible game code, but turning this capability into iterative creative improvement remains difficult. In practice,
arXiv:2604.20083v1 Announce Type: cross Abstract: Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a
arXiv:2603.09283v2 Announce Type: replace Abstract: Removing objects from videos remains difficult in the presence of real-world imperfections such as shadows, abrupt motion, and defective masks. Exis
arXiv:2604.20140v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is an effective framework for aligning large language models with human preferences, but it struggles with complex