Knowledge catalogue
arXiv:2604.11674v1 Announce Type: cross Abstract: Simulation-based data generation has become a dominant paradigm for training robotic manipulation policies, yet existing platforms do not incorporate
arXiv:2601.03641v4 Announce Type: replace Abstract: Large Language Model (LLM)-based agents significantly extend the utility of LLMs by interacting with dynamic environments. However, enabling agents
arXiv:2604.10513v1 Announce Type: new Abstract: AI agent development relies heavily on natural language prompting to define agents' tasks, knowledge, and goals. These prompts are interpreted by Large
arXiv:2604.11705v1 Announce Type: new Abstract: Foundation models, including large language models (LLMs), are increasingly used for human-in-the-loop (HITL) cyber-physical systems (CPS) because found
arXiv:2604.09584v1 Announce Type: new Abstract: Flow physics and more broadly physical phenomena governed by partial differential equations (PDEs), are inherently continuous, high-dimensional and ofte
arXiv:2601.04672v2 Announce Type: replace-cross Abstract: Agricultural disease diagnosis challenges VLMs, as conventional fine-tuning requires extensive labels, lacks interpretability, and generalizes
arXiv:2604.11065v1 Announce Type: new Abstract: AI systems increasingly shape high-stakes decisions in healthcare, law, defense, and education, yet existing governance paradigms -- AI Ethics, AI Safet
arXiv:2604.11135v1 Announce Type: cross Abstract: Pretrained video generation models provide strong priors for robot control, but existing unified world action models still struggle to decode reliable
arXiv:2506.07523v3 Announce Type: replace Abstract: Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their answers. Yet the features driving an ans
arXiv:2604.11730v1 Announce Type: new Abstract: Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits
arXiv:2604.10805v1 Announce Type: new Abstract: Accurate distance estimation from monocular cameras is essential for intelligent monitoring systems. In many deployments, image coordinates are mapped t
arXiv:2604.11754v1 Announce Type: cross Abstract: In this work, we study angle-based localization and rigidity maintenance control for multi-robot networks under sensing constraints. We establish the
arXiv:2604.10432v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have emerged as a versatile paradigm for generalist robotic manipulation. However, precise object placement under
arXiv:2604.10874v1 Announce Type: cross Abstract: Adverse Outcome Pathways (AOPs) are an important knowledge framework in toxicological research and risk assessment. In recent years, large language mo
arXiv:2604.10992v1 Announce Type: new Abstract: Parametric Computer-Aided Design (CAD) of articulated assemblies is essential for product development, yet generating these multi-part, movable models f
arXiv:2604.10065v1 Announce Type: cross Abstract: End-to-end full-duplex Speech Language Models (SLMs) require precise turn-taking for natural interaction. However, optimizing temporal dynamics via st
arXiv:2604.09695v1 Announce Type: cross Abstract: The increasing use of Online Vision Language Models (OVLMs) for processing images has introduced significant privacy risks, as individuals frequently
arXiv:2604.10766v1 Announce Type: new Abstract: Open-set 3D macromolecule detection in cryogenic electron tomography eliminates the need for target-specific model retraining. However, strict VRAM cons
arXiv:2604.10078v1 Announce Type: new Abstract: Student engagement is crucial for improving learning outcomes in group activities. Highly engaged students perform better both individually and contribu
arXiv:2604.09700v1 Announce Type: cross Abstract: Constructing high-resolution 3D geological models from sparse 1D borehole and 2D surface data is a highly ill-posed inverse problem. Traditional heuri
arXiv:2604.10697v1 Announce Type: new Abstract: Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context.
arXiv:2508.03793v2 Announce Type: replace Abstract: Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, includi
arXiv:2604.09866v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have shown the promise to significantly accelerate the workflow by automating structural modeling and
arXiv:2604.09748v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model's (LLM's) reasoning abi
arXiv:2604.09686v1 Announce Type: new Abstract: Traditional neural network models for intent inference rely heavily on observable states and struggle to generalize across diverse tasks and dynamic env
arXiv:2604.11714v1 Announce Type: new Abstract: Pretrained detectors perform well on benchmarks but often suffer performance degradation in real-world deployments due to distribution gaps between trai
arXiv:2604.11665v1 Announce Type: cross Abstract: This paper reports an unexpected finding: in a deterministic hyperdimensional computing (HDC) architecture based on Galois-field algebra, a path-depen
arXiv:2603.22153v3 Announce Type: replace-cross Abstract: Recent advances in cross-view geo-localization (CVGL) methods have shown strong potential for supporting unmanned aerial vehicle (UAV) navigat
arXiv:2602.02007v3 Announce Type: replace-cross Abstract: Agent memory systems often adopt the standard Retrieval-Augmented Generation (RAG) pipeline, yet its underlying assumptions differ in this set
arXiv:2604.05767v2 Announce Type: replace-cross Abstract: We present BADAS-2.0, the second generation of our collision anticipation system, building on BADAS-1.0, which showed that fine-tuning V-JEPA2
arXiv:2603.08942v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized
arXiv:2603.14410v3 Announce Type: replace Abstract: Generating long-form linear fiction from open-ended themes remains a major challenge for large language models, which frequently fail to guarantee g
arXiv:2604.09927v1 Announce Type: new Abstract: Robust license plate recognition in unconstrained environments remains a significant challenge, particularly in underrepresented regions with limited da
arXiv:2604.10389v1 Announce Type: new Abstract: Terminology substitution errors in clinical notes, where one medical term is replaced by a linguistically valid but clinically different term, pose a pe
arXiv:2604.10950v1 Announce Type: new Abstract: Fully supervised Video Semantic Segmentation (VSS) relies heavily on densely annotated video data, limiting practical applicability. Alternatively, appl
NVIDIA's ALCHEMI Toolkit is a PyTorch-native framework that lets researchers build custom GPU-accelerated atomistic simulation workflows for chemistry and materials science applications. The initial r
arXiv:2604.11010v1 Announce Type: new Abstract: Digital forensic investigations often face significant challenges when recovering fragmented multimedia files that lack file system metadata. While trad
arXiv:2604.09691v1 Announce Type: cross Abstract: Educational diagrams -- labeled illustrations of biological processes, chemical structures, physical systems, and mathematical concepts -- are essenti
arXiv:2604.10696v1 Announce Type: new Abstract: We present Camyla, a system for fully autonomous research within the scientific domain of medical image segmentation. Camyla transforms raw datasets int
arXiv:2505.18931v4 Announce Type: replace Abstract: Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models
arXiv:2512.24503v2 Announce Type: replace-cross Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scal
arXiv:2601.06993v2 Announce Type: replace Abstract: Multi-modal large language models (MLLMs) exhibit strong general-purpose capabilities, yet still struggle on Fine-Grained Visual Classification (FGV
arXiv:2604.11202v1 Announce Type: cross Abstract: We present CapBench, a fully reproducible, multi-PDK dataset for capacitance extraction. The dataset is derived from open-source designs, including si
arXiv:2604.10420v1 Announce Type: new Abstract: Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on wea
arXiv:2509.17458v3 Announce Type: replace-cross Abstract: Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional
arXiv:2604.10502v1 Announce Type: new Abstract: Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of tradi
arXiv:2604.09611v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in applications forming multi-request workflows like document summarization, search-based copilots,
arXiv:2604.10114v1 Announce Type: cross Abstract: The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from ha
arXiv:2604.11320v1 Announce Type: new Abstract: Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show str
arXiv:2604.10305v1 Announce Type: cross Abstract: Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyon
ClawBench is a benchmark of 153 everyday web tasks spanning 144 live platforms across 15 categories — from completing purchases and booking appointments to submitting job applications. Unlike existing
arXiv:2604.11641v1 Announce Type: cross Abstract: Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stag
arXiv:2604.01687v2 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool
arXiv:2604.09621v1 Announce Type: new Abstract: We present an agent-driven approach to the construction of parameter inference pipelines for scientific data analysis. Our method leverages a multi-agen
arXiv:2604.10875v1 Announce Type: cross Abstract: AI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide a
arXiv:2604.09722v1 Announce Type: cross Abstract: Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from he
arXiv:2604.09750v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives rema
arXiv:2601.11310v2 Announce Type: replace Abstract: Semantic ultra-high-resolution (UHR) image segmentation is essential in remote sensing applications such as aerial mapping and environmental monitor
arXiv:2511.01763v2 Announce Type: replace-cross Abstract: Binary decompilation plays an important role in software security analysis, reverse engineering, and malware understanding when source code is
arXiv:2604.11521v1 Announce Type: cross Abstract: We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which