Knowledge catalogue
arXiv:2604.27126v1 Announce Type: new Abstract: This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce.
arXiv:2411.15253v2 Announce Type: replace-cross Abstract: Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure,
arXiv:2604.27231v1 Announce Type: cross Abstract: Freelance workers must continually acquire new skills to remain competitive in online labor markets, yet they lack the organizational training, mentor
arXiv:2604.27093v1 Announce Type: cross Abstract: Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulnes
arXiv:2604.27017v1 Announce Type: cross Abstract: Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required
arXiv:2604.26983v1 Announce Type: cross Abstract: This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-
arXiv:2510.09696v2 Announce Type: replace-cross Abstract: The increasing scale of Deep Neural Networks (DNNs) increases the need for compression techniques such as pruning, quantization, and low-rank
arXiv:2406.04808v2 Announce Type: replace Abstract: Two-dimensional embeddings obtained from dimensionality reduction techniques such as MDS, t-SNE, or UMAP, are widely used to visualize high-dimensio
arXiv:2604.27375v1 Announce Type: new Abstract: Reasoning photo retouching has gained significant traction, requiring models to analyze image defects, give reasoning processes, and execute precise ret
arXiv:2601.08611v2 Announce Type: replace-cross Abstract: The growing scale of online misinformation urgently demands Automated Fact-Checking (AFC). Existing benchmarks for evaluating AFC systems, how
arXiv:2502.02097v3 Announce Type: replace Abstract: This aims to develop and validate a deep learning model that can accurately locate vertebral landmarks in lateral spine Dual energy X-ray Absorptiom
arXiv:2604.27685v1 Announce Type: cross Abstract: While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computat
arXiv:2411.18929v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models learn to remove noise added during training, generating novel data (e.g., images) from Gaussian noise through s
arXiv:2506.05412v3 Announce Type: replace-cross Abstract: Where someone looks is a nonverbal communication cue that children and adults readily use. How well can Vision-Language Models (VLMs) infer ga
arXiv:2408.02679v3 Announce Type: replace Abstract: We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcom
arXiv:2604.28185v1 Announce Type: new Abstract: Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still str
arXiv:2604.27259v1 Announce Type: new Abstract: Time-series classification (TSC) has advanced significantly with deep learning, yet most models rely solely on raw numerical inputs, overlooking alterna
arXiv:2604.27629v1 Announce Type: new Abstract: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models. To address data scarcity in semiconduct
arXiv:2604.27221v1 Announce Type: new Abstract: Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and het
arXiv:2508.13024v3 Announce Type: replace Abstract: LLM-based web agents have the potential to automate long-running web tasks, such as searching for products in multiple e-shops and subsequently orde
arXiv:2604.28093v1 Announce Type: new Abstract: Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the
arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe
arXiv:2604.27272v1 Announce Type: cross Abstract: Large language models (LLMs) conventionally process structured inputs as 1D token sequences. While natural for prose, such linearization may introduce
arXiv:2604.27691v1 Announce Type: new Abstract: Across millennia, complex societies have faced the same coordination problem of how to organize collective action among cognitively bounded and informat
arXiv:2604.27003v1 Announce Type: cross Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in ex
arXiv:2604.27656v1 Announce Type: cross Abstract: To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowle
arXiv:2604.27228v1 Announce Type: new Abstract: Democratic discourse analysis systems increasingly rely on multi-agent LLM pipelines in which distinct evaluator models are assigned adversarial roles t
arXiv:2604.27082v1 Announce Type: new Abstract: We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires repla
arXiv:2604.27398v1 Announce Type: new Abstract: For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actu
arXiv:2604.27743v1 Announce Type: cross Abstract: We develop a geometric and information-theoretic framework for encoder-decoder learning built on the Information Bottleneck (IB) principle. Recasting
arXiv:2604.27776v1 Announce Type: new Abstract: While GUI agents have shown impressive capabilities in common computer-use tasks such as OSWorld, current benchmarks mainly focus on isolated and single
arXiv:2604.27578v1 Announce Type: new Abstract: Embodied intelligence requires high-fidelity simulation environments to support perception and decision-making, yet existing platforms often suffer from
arXiv:2602.02980v2 Announce Type: replace-cross Abstract: In this work, we focus on front-end design for speech deepfake detectors, the component that determines the discriminative acoustic cues provi
arXiv:2604.27322v1 Announce Type: new Abstract: Recent advances in Diffusion Transformer (DiT)-based video generation technologies have shown impressive results for video object removal. However, thes
arXiv:2604.27606v1 Announce Type: cross Abstract: Learning informative representations from tabular data in remote sensing and environmental science is challenging due to heterogeneity, scarce labels,
arXiv:2604.27844v1 Announce Type: cross Abstract: Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been pr
arXiv:2604.26212v1 Announce Type: new Abstract: In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Fe
arXiv:2604.26509v1 Announce Type: cross Abstract: Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-wo
arXiv:2604.26520v1 Announce Type: new Abstract: Aerial-Ground Re-Identification (AG-ReID) is constrained by the viewpoint-domain gap, as drastic viewpoint disparities occlude or distort discriminative
arXiv:2604.26147v1 Announce Type: cross Abstract: Accurate intraoperative assessment of glioma infiltration is essential for maximizing tumor resection while preserving functional brain tissue. Fluore
arXiv:2601.18339v2 Announce Type: replace-cross Abstract: The Complete Vocal Technique (CVT) is a school of singing developed in the past decades by Cathrin Sadolin et al.. CVT groups the use of the v
arXiv:2602.23163v3 Announce Type: replace Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanis
arXiv:2603.11806v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel mathematical framework for piecewise diffeomorphic image registration that involves discontinuous sliding mo
arXiv:2604.26351v1 Announce Type: new Abstract: Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such a
arXiv:2604.26807v1 Announce Type: new Abstract: Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent
arXiv:2604.25942v1 Announce Type: new Abstract: Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We
arXiv:2510.08278v3 Announce Type: replace Abstract: Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues. While
arXiv:2604.26379v1 Announce Type: new Abstract: Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is lab
arXiv:2604.26462v1 Announce Type: new Abstract: Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows.
arXiv:2604.26230v1 Announce Type: new Abstract: I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns pol
arXiv:2604.26926v1 Announce Type: new Abstract: In Orabona and Pal [2016], we introduced the shifted KT potentials, to remove the ln ln T factor in the parameter-free learning with expert bound. In th
arXiv:2409.06624v4 Announce Type: replace-cross Abstract: Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The hug
arXiv:2603.25980v2 Announce Type: replace-cross Abstract: Transition states, the first-order saddle points on the potential energy surfaces, govern the kinetics and mechanisms of chemical reactions an
arXiv:2506.05878v2 Announce Type: replace Abstract: We present a feasibility-seeking approach to neural network training. This mathematical optimization framework is distinct from conventional gradien
arXiv:2604.25979v1 Announce Type: cross Abstract: We present a quantitative analysis of character-pair substitution ratios in the Voynich manuscript, testing whether Currier's A/B language distinction
arXiv:2604.25943v1 Announce Type: cross Abstract: Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical s
arXiv:2604.25963v1 Announce Type: new Abstract: Vehicle platooning has attracted increasing attention as a promising approach to improve traffic efficiency, energy consumption, and roadway safety thro
arXiv:2604.25933v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which
arXiv:2604.07387v2 Announce Type: replace-cross Abstract: We present a design automation framework for analog circuit sizing that produces calibrated, topology-specific analytical equations from raw c
arXiv:2604.26703v1 Announce Type: cross Abstract: Standard density functional theory (DFT) routinely misclassifies the electronic ground state of correlated and structurally complex compounds, predict