Knowledge catalogue
arXiv:2606.22454v2 Announce Type: replace Abstract: As LLM-generated text is increasingly used, especially in fictional domains, we explore how much LLM-generated stories differ from human-written sto
arXiv:2608.27797v1 Announce Type: cross Abstract: Natural-language tasking of embodied agents is rarely just goal specification: users also impose constraints that must persist while the world changes
arXiv:2608.28070v1 Announce Type: new Abstract: Automated detection of surface micro-defects on industrial components, such as copper tubes, is critically important for quality assurance but remains c
arXiv:2608.28570v1 Announce Type: new Abstract: Identifying the underlying dynamics and 3D geometry of deformable linear objects (DLOs), such as cables, ropes, and hoses, is essential for accurate rob
arXiv:2608.28053v1 Announce Type: new Abstract: Chinese neologisms exploit diverse and unique linguistic mechanisms, such as phonetic substitution (e.g., 886 for ``bye-bye'') and visual character deco
arXiv:2608.28140v1 Announce Type: new Abstract: Non-prehensile manipulation offers versatile skills for moving and rearranging heavy or bulky objects, particularly when combined with a mobile manipula
arXiv:2608.28409v1 Announce Type: new Abstract: Multi-robot systems are well-positioned for exploration in hazardous environments, but effective deployment requires deciding not only where robots shou
arXiv:2608.28405v1 Announce Type: new Abstract: Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common u
arXiv:2608.27634v1 Announce Type: cross Abstract: Nearest neighbor classification relies fundamentally on how locality is defined, yet conventional k-NN imposes the same neighborhood cardinality throu
arXiv:2608.27713v1 Announce Type: new Abstract: Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand ca
arXiv:2603.27936v3 Announce Type: replace-cross Abstract: Nonlinear Partial Differential Equations (PDEs) are ubiquitous in mathematical physics and engineering. Although Physics-Informed Neural Netwo
arXiv:2502.15438v5 Announce Type: replace Abstract: Vision-based occupancy networks (VONs) provide an end-to-end solution for reconstructing 3D environments in autonomous driving. However, existing me
arXiv:2608.28343v1 Announce Type: new Abstract: High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal complet
arXiv:2608.27633v1 Announce Type: new Abstract: Pothole detection and its severity measurement is still an important challenges in urban infrastructure management, where late maintenance directly cont
arXiv:2608.27516v1 Announce Type: cross Abstract: Deep learning's diagnostic utility in pathology is constrained by model vulnerability to real-world data imperfections. While current strategies favor
arXiv:2206.09012v4 Announce Type: replace-cross Abstract: We consider the problem of inferring high-dimensional data mathbf{x} in a model that consists of a prior p(mathbf{x}) and an auxiliary differe
arXiv:2608.28283v1 Announce Type: new Abstract: In computational argumentation, obtaining arguments that explicitly support or attack given claims is a critical precursor to downstream reasoning tasks
arXiv:2608.28207v1 Announce Type: new Abstract: Diabetic retinopathy (DR) is a major cause of preventable blindness, creating a need for accurate and trustworthy automated screening. This study invest
arXiv:2608.28191v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) are widely used in computational pathology but remain sensitive to domain shifts arising from variations in staining, ti
arXiv:2608.27763v1 Announce Type: cross Abstract: Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state tran
arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling
arXiv:2608.28155v1 Announce Type: new Abstract: Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM
arXiv:2608.28218v1 Announce Type: new Abstract: Vision-language models (VLMs) are rapidly progressing and offer promising capabilities for assistive technologies supporting persons with blindness or l
arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.
arXiv:2608.27527v1 Announce Type: new Abstract: A major challenge in finger vein recognition is the lack of large-scale public datasets. Existing datasets contain few identities and limited samples pe
arXiv:2608.28288v1 Announce Type: new Abstract: Existing feed-forward 3D reconstruction methods typically process a bounded number of images and recover cameras and geometry in local or internally nor
arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic ob
arXiv:2506.07188v2 Announce Type: replace Abstract: End-to-end neural networks have become a dominant paradigm in autonomous driving, where reliable deployment requires controllable post-training adap
arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies
arXiv:2305.19366v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the p
arXiv:2608.28273v1 Announce Type: new Abstract: Urban transportation systems consist of multiple mobility modes that coexist within the same city and exhibit complex interdependencies, leading to corr
arXiv:2608.27719v1 Announce Type: new Abstract: Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to cap
arXiv:2608.28435v1 Announce Type: new Abstract: Many robotic tasks are temporally extended and demand precise specifications of subgoals, constraints, and their temporal ordering. Yet human operators
arXiv:2608.28375v1 Announce Type: cross Abstract: Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observat
arXiv:2608.28300v1 Announce Type: new Abstract: Robotic industrial panel operation requires not only accurate control localization but also compliance with operating procedures, safety rules, and devi
arXiv:2602.17654v2 Announce Type: replace-cross Abstract: Embedding-based retrieval (EBR) for large-scale e-commerce search faces three intertwined challenges: graded (non-binary) relevance where enga
arXiv:2509.26536v3 Announce Type: replace Abstract: We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding r
arXiv:2608.28566v1 Announce Type: cross Abstract: We study the mixing time of weighted Dikin walks for sampling from exponential distributions on polytopes and truncated positive-semidefinite (PSD) co
arXiv:2608.27899v1 Announce Type: new Abstract: With the growing prevalence of large language model (LLM) generated content, watermarking is considered a promising approach for attributing text to LLM
arXiv:2608.28198v1 Announce Type: new Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in t
arXiv:2608.27816v1 Announce Type: new Abstract: Personalization has attracted growing interest in LLM applications, yet existing retrieval-based approaches depend heavily on retrieval quality and degr
arXiv:2608.28378v1 Announce Type: new Abstract: Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally comp
arXiv:2608.27609v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping language instructions and vision obse
arXiv:2608.27978v1 Announce Type: new Abstract: Battery health prognostics is a core function in battery management systems (BMSs), yet long-horizon health forecasting from BMS signals remains challen
arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly grow
arXiv:2608.27512v1 Announce Type: cross Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision
arXiv:2608.27974v1 Announce Type: new Abstract: Data annotation remains a central bottleneck in natural language processing, requiring human effort to obtain high-quality labels at scale. While Large
arXiv:2608.28219v1 Announce Type: new Abstract: Cross-identity character animation aims to drive a target identity from a reference image to follow the motion of a source character from a driving vide
arXiv:2608.28366v1 Announce Type: cross Abstract: State estimation in magnetohydrodynamic flows is critical for real-time monitoring of liquid metal blankets in tokamak fusion reactors. Due to the mul
arXiv:2608.28371v1 Announce Type: new Abstract: Real-time musculoskeletal (MSK) surrogates could support personalized rehabilitation for children with cerebral palsy (CP), but their credibility depend
arXiv:2503.11072v4 Announce Type: replace Abstract: Time-optimal trajectory planning and control is central for autonomous vehicles, yet its application and real-time deployment confronts two fundamen
arXiv:2608.28499v1 Announce Type: new Abstract: To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adv
arXiv:2608.27668v1 Announce Type: new Abstract: Segmentation models can surpass radiologists, classification models, and vision-language models in tumor detection. Importantly, segmentation models out
arXiv:2608.28267v1 Announce Type: new Abstract: Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the out
arXiv:2407.04291v4 Announce Type: replace-cross Abstract: Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech syst
arXiv:2602.10696v3 Announce Type: replace-cross Abstract: Assortment optimization is a fundamental challenge in modern retail and recommendation systems, where the goal is to select a subset of produc
arXiv:2607.17018v2 Announce Type: replace-cross Abstract: We study distributionally robust linear chance-constrained problems in which uncertainty is modeled by a Gaussian mixture model (GMM). Finite-
arXiv:2604.06079v2 Announce Type: replace Abstract: Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals i
arXiv:2603.21840v2 Announce Type: replace Abstract: Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly exp
arXiv:2608.27574v1 Announce Type: new Abstract: Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups