Knowledge catalogue
arXiv:2508.06094v4 Announce Type: replace Abstract: Constructed languages (conlangs) such as Esperanto and Quenya have played diverse roles in art, philosophy, and international communication. Meanwhi
arXiv:2604.15547v1 Announce Type: cross Abstract: The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflic
arXiv:2604.16061v1 Announce Type: cross Abstract: We study discrete k-clustering problems in general metric spaces that are constrained by a combination of two different fairness conditions within the
arXiv:2604.15638v1 Announce Type: new Abstract: Continuum robots are well suited for navigating confined and fragile environments, such as vascular or endoluminal anatomy, where contact with surroundi
arXiv:2604.05552v2 Announce Type: replace-cross Abstract: Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-lin
arXiv:2604.15814v1 Announce Type: new Abstract: Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learni
arXiv:2604.15791v1 Announce Type: new Abstract: The quantification of uncertainty in prediction models is crucial for reliable decision-making, yet remains a significant challenge. Interval time serie
arXiv:2604.16008v1 Announce Type: new Abstract: This paper introduces a robust discrimination method for distinguishing real ship targets from corner-reflector-array jamming with frequency-agile radar
arXiv:2512.01099v2 Announce Type: replace Abstract: As modern artificial intelligence (AI) systems become more advanced and capable, they can leverage a wide range of tools and models to perform compl
arXiv:2510.03875v2 Announce Type: replace Abstract: The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Se
arXiv:2604.15665v1 Announce Type: new Abstract: Markerless 3D movement analysis from monocular video enables accessible biomechanical assessment in clinical and sports settings. However, most research
arXiv:2504.04927v2 Announce Type: replace-cross Abstract: As generative AI (GenAI) is increasingly applied in persona development to represent real users, understanding the implications and limitation
arXiv:2603.20210v3 Announce Type: replace-cross Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependen
arXiv:2603.16427v2 Announce Type: replace Abstract: This paper considers self-supervised cross-modal coordination as a strategy enabling utilization of multiple modalities and large volumes of unlabel
arXiv:2604.15590v1 Announce Type: cross Abstract: Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement lea
arXiv:2604.15561v1 Announce Type: cross Abstract: Reported chest CT segmentation performance can be strongly inflated when train and test partitions mix slices from the same study. We present CTSCAN,
arXiv:2604.15768v1 Announce Type: cross Abstract: AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrodinger equation for complex
arXiv:2510.07774v3 Announce Type: replace Abstract: In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning abil
arXiv:2604.16029v1 Announce Type: new Abstract: Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, pat
arXiv:2604.15555v1 Announce Type: new Abstract: Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing
arXiv:2604.15593v1 Announce Type: cross Abstract: Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during genera
arXiv:2406.14294v3 Announce Type: replace-cross Abstract: Discrete audio tokens have recently gained considerable attention for their potential to bridge audio and language processing, enabling multim
arXiv:2604.15594v1 Announce Type: cross Abstract: Modern datacenters schedule heterogeneous workloads across geo-distributed sites with diverse compute capacities, electricity prices, and thermal cond
arXiv:2510.20299v3 Announce Type: replace-cross Abstract: Brain tumors are a challenging problem in neuro-oncology, where early and precise diagnosis is important for successful treatment. Deep learni
arXiv:2403.18026v2 Announce Type: replace-cross Abstract: High-throughput biological imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, s
arXiv:2604.15456v1 Announce Type: new Abstract: Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent d
arXiv:2507.16727v3 Announce Type: replace Abstract: Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose extbf{De
arXiv:2604.16201v1 Announce Type: cross Abstract: Consumer LiDARs in mobile devices and robots typically output a single depth value per pixel. Yet internally, they record full time-resolved histogram
arXiv:2604.16099v1 Announce Type: new Abstract: Tables condense key transactional and administrative information into compact layouts, but practical extraction requires more than text recognition: sys
arXiv:2604.16231v1 Announce Type: new Abstract: Panoramic radiography is a fundamental diagnostic tool in dentistry, offering a comprehensive view of the entire dentition with minimal radiation exposu
arXiv:2604.15750v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive language generation due to their potential for parallel dec
arXiv:2503.16505v4 Announce Type: replace-cross Abstract: A critical challenge in social science research is the high cost associated with experiments involving human participants. We identify Synthet
arXiv:2604.16242v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning.
arXiv:2603.01098v2 Announce Type: replace-cross Abstract: Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of pri
arXiv:2604.15723v1 Announce Type: cross Abstract: The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective.
arXiv:2604.16083v1 Announce Type: new Abstract: With the rapid advancement of deep generative models, realistic fake images have become increasingly accessible, yet existing localization methods rely
arXiv:2604.15839v1 Announce Type: new Abstract: Most ATP benchmarks embed the final answer within the formal statement -- a convention we call 'Easy Mode' -- a design that simplifies the task relative
arXiv:2604.16015v1 Announce Type: cross Abstract: Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent
arXiv:2604.15842v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain unde
arXiv:2604.15408v1 Announce Type: cross Abstract: Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet when prune
arXiv:2510.21977v2 Announce Type: replace Abstract: Large language models (LLMs) offer a promising way to simulate human survey responses, potentially reducing the cost of large-scale data collection.
arXiv:2604.15866v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER), yet their ge
arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr
arXiv:2604.16256v1 Announce Type: cross Abstract: Reasoning in vision-language models (VLMs) has recently attracted significant attention due to its broad applicability across diverse downstream tasks
arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve
arXiv:2512.23421v3 Announce Type: replace Abstract: World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the rea
arXiv:2604.16047v1 Announce Type: cross Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accel
arXiv:2604.15865v1 Announce Type: new Abstract: Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition betwe
arXiv:2604.16104v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo
arXiv:2509.09530v2 Announce Type: replace Abstract: Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost a
arXiv:2506.13215v2 Announce Type: replace Abstract: Recently, patch deformation-based methods have demonstrated significant effectiveness in multi-view stereo due to their incorporation of deformable
arXiv:2505.16176v2 Announce Type: replace Abstract: In mathematical reasoning, data selection strategies predominantly rely on static, externally defined metrics, which fail to adapt to the evolving c
arXiv:2512.17052v4 Announce Type: replace Abstract: Function calling agents powered by Large Language Models (LLMs) select external tools to automate complex tasks. On-device agents typically use a re
arXiv:2506.03103v2 Announce Type: replace Abstract: Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains chal
arXiv:2604.16082v1 Announce Type: cross Abstract: Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a ch
arXiv:2604.15822v1 Announce Type: cross Abstract: Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compare
arXiv:2509.14977v2 Announce Type: replace Abstract: Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, a
arXiv:2604.15327v1 Announce Type: cross Abstract: Universities are microcosms of urban ecosystems, with concentrated consumption patterns in food, transport, energy, and product usage. These environme
arXiv:2604.15911v1 Announce Type: new Abstract: Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains co
arXiv:2604.16044v1 Announce Type: new Abstract: Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these mo