Knowledge catalogue
arXiv:2605.19518v1 Announce Type: new Abstract: Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify sem
arXiv:2605.19972v1 Announce Type: cross Abstract: Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compresse
arXiv:2605.19649v1 Announce Type: new Abstract: Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits appli
arXiv:2605.19837v1 Announce Type: cross Abstract: Adverse weather (rain, fog, sand, and snow) degrades camera-based object detection in autonomous vehicles. Existing enhancement-then-detect approaches
arXiv:2605.19132v1 Announce Type: new Abstract: The electrocardiogram (ECG) is the gold standard for non-invasive diagnosis of cardiac pathologies and is a fundamental pillar of cardiovascular medicin
arXiv:2605.20176v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has a
arXiv:2605.19490v1 Announce Type: cross Abstract: Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based t
arXiv:2605.19758v1 Announce Type: new Abstract: The ability to maintain and manipulate information over time is a fundamental aspect of living beings and Artificial Intelligence. While modern models h
arXiv:2509.14787v2 Announce Type: replace Abstract: Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. E
arXiv:2605.19624v1 Announce Type: cross Abstract: Monocular 6D pose estimation for non-cooperative satellites depends heavily on annotated training data, yet real satellite images with reliable pose l
arXiv:2605.18808v1 Announce Type: cross Abstract: We characterize a compositional architecture of literary primitives in two instruction-tuned large language models (Llama 3.1 8B-Instruct and Gemma 2
arXiv:2603.11673v2 Announce Type: replace Abstract: Many physical systems exhibit a low-dimensional structure that varies with external parameters: link lengths in a robot, forcing constants in a flui
arXiv:2605.20075v1 Announce Type: cross Abstract: Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm
arXiv:2605.19092v1 Announce Type: cross Abstract: Reasoning systems increasingly separate intermediate computation into private and public channels, creating evaluation cases that look similar in tran
arXiv:2605.19299v1 Announce Type: new Abstract: The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data charact
arXiv:2605.18773v1 Announce Type: cross Abstract: Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment w
arXiv:2605.19737v1 Announce Type: cross Abstract: Digital Twin (DT) technology holds immense potential for surgical planning and personalized medicine. However, generating interactive, patient-specifi
arXiv:2105.00933v3 Announce Type: replace-cross Abstract: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the
arXiv:2605.19228v1 Announce Type: cross Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing
arXiv:2509.21196v3 Announce Type: replace-cross Abstract: Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applicati
arXiv:2605.19621v1 Announce Type: cross Abstract: Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical
arXiv:2602.23622v2 Announce Type: replace-cross Abstract: Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate pla
arXiv:2605.18915v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-
arXiv:2605.19293v1 Announce Type: cross Abstract: Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scal
arXiv:2605.20104v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, rece
arXiv:2605.19726v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoreg
arXiv:2511.19741v3 Announce Type: replace Abstract: Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems
arXiv:2604.02784v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or unground
arXiv:2410.18856v4 Announce Type: replace Abstract: Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools c
arXiv:2605.18918v1 Announce Type: cross Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web s
arXiv:2605.18908v1 Announce Type: cross Abstract: Deep neural networks (DNNs) remain critically vulnerable to backdoor attacks. Existing post-training detectors often require clean or surrogate data,
arXiv:2605.20132v1 Announce Type: cross Abstract: Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of convent
arXiv:2507.10614v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLM
arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the
arXiv:2503.11615v3 Announce Type: replace Abstract: Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current
arXiv:2605.18865v1 Announce Type: cross Abstract: Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensiv
arXiv:2605.18921v1 Announce Type: new Abstract: High-definition (HD) maps are core artifacts for automated driving systems, but their generation commonly relies on sensor-intensive mobile mapping camp
arXiv:2605.19889v1 Announce Type: cross Abstract: 3D Lookup Tables (3D LUTs) are widely used for color mapping, but their grid-based representation requires discretizing the RGB space, leading to a ca
arXiv:2605.19190v1 Announce Type: cross Abstract: Despite the global deployment of text-to-image (T2I) models, their safety frameworks are largely calibrated to a Western-centric default, creating sig
arXiv:2605.19577v1 Announce Type: new Abstract: We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR
arXiv:2605.05480v2 Announce Type: replace-cross Abstract: The main XAI attribution methods for deep neural networks -- GradCAM, SHAP, LIME, Integrated Gradients -- operate on separate theoretical foun
arXiv:2605.19733v1 Announce Type: cross Abstract: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years
arXiv:2605.18974v1 Announce Type: cross Abstract: Classifying artworks presents a significant challenge due to the complex interplay of fine-grained details and abstract features that condition the st
arXiv:2605.18822v1 Announce Type: cross Abstract: Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, prefere
arXiv:2605.18932v1 Announce Type: cross Abstract: In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a
arXiv:2605.19592v1 Announce Type: cross Abstract: Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deploym
arXiv:2508.16112v2 Announce Type: replace Abstract: Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an imp
arXiv:2605.20108v1 Announce Type: cross Abstract: While conventional (k=1) discrete-time barrier certificate conditions impose strict safety constraints by requiring the function to be non-increasing
arXiv:2605.19645v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often pres
arXiv:2605.18895v1 Announce Type: cross Abstract: Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attribut
arXiv:2605.19049v1 Announce Type: cross Abstract: Linear attention has recently gained significant attention for long-context inference due to its constant decoding cost with respect to context length
arXiv:2605.19008v1 Announce Type: new Abstract: Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, sca
arXiv:2510.27588v3 Announce Type: replace-cross Abstract: We consider the task of constructing a data structure for associating a static set of keys with values, while allowing arbitrary output values
arXiv:2605.19113v1 Announce Type: cross Abstract: Many clinical risk scores are deployed as additive rules with nonnegative integer points assigned to relevant binary predictive features. These intege
arXiv:2605.19074v1 Announce Type: cross Abstract: The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting mode
arXiv:2605.19959v1 Announce Type: new Abstract: Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear a
arXiv:2605.20149v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interacti
arXiv:2504.00470v2 Announce Type: replace-cross Abstract: To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of exis
arXiv:2605.19999v1 Announce Type: cross Abstract: Benchmark datasets are critical for reproducible, reliable, and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark
arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r