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arXiv:2603.04384v1 Announce Type: new Abstract: Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike human users who issue and refine...
arXiv:2603.04385v1 Announce Type: new Abstract: Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and $\pi^3$ have a...
arXiv:2603.04390v1 Announce Type: new Abstract: WebGIS development requires rigor, yet agentic AI frequently fails due to five large language model (LLM) limitations: context constraints, cross-session...
arXiv:2603.04393v1 Announce Type: new Abstract: In this work, we present bayesgrid, an open-source python toolbox for generating synthetic power transmission-distribution systems for any geographical location...
arXiv:2603.04395v1 Announce Type: new Abstract: Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing...
arXiv:2603.04399v1 Announce Type: new Abstract: Human motion prediction combines the tasks of trajectory forecasting and human pose prediction. For each of the two tasks, specialized...
arXiv:2502.03300v2 Announce Type: cross Abstract: Wi-Fi 7 introduces the restricted target wake time (RTWT) mechanism, which is vital for Industrial IoT (IIoT) applications requiring periodic,...
arXiv:2603.03342v1 Announce Type: cross Abstract: Learning robust representations of 3D shapes from voxelized data is essential for advancing AI methods in biomedical imaging. However, most...
arXiv:2603.03343v1 Announce Type: cross Abstract: We propose NEURONA, a neuro-symbolic framework for fMRI decoding and concept grounding in neural activity. Leveraging image- and video-based fMRI...
arXiv:2603.03344v1 Announce Type: cross Abstract: Earthquake detection and seismic phase picking are fundamental yet challenging tasks in seismology due to low signal-to-noise ratios, waveform variability,...
arXiv:2603.03346v1 Announce Type: cross Abstract: Modeling the unsaturated behavior of porous materials with multimodal pore size distributions presents significant challenges, as standard hydraulic models often...
arXiv:2603.03350v1 Announce Type: cross Abstract: Manual measurement of muscle morphology from ultrasound during speech is time-consuming and limits large-scale studies. We present SMMA, a fully...
arXiv:2603.03352v1 Announce Type: cross Abstract: The International Physics Olympiad (IPhO) is the world's most prestigious and renowned physics competition for pre-university students. IPhO problems require...
arXiv:2603.03354v1 Announce Type: cross Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity...
arXiv:2603.03355v1 Announce Type: cross Abstract: We present a memory-augmented transformer in which attention serves simultaneously as a retrieval, consolidation, and write-back operator. The core update,...
arXiv:2603.03366v1 Announce Type: cross Abstract: We consider the implementation of optimization techniques within the study of tectonic plate motion. Specifically, we examine the optimization underlying...
arXiv:2603.03372v1 Announce Type: cross Abstract: Density Functional Theory (DFT) is a cornerstone of materials science, yet executing DFT in practice requires coordinating a complex, multi-step...
arXiv:2603.03375v1 Announce Type: cross Abstract: In 2018, McInnes et al. introduced a dimensionality reduction algorithm called UMAP, which enjoys wide popularity among data scientists. Their...
arXiv:2603.03387v1 Announce Type: cross Abstract: Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this...
arXiv:2603.03401v1 Announce Type: cross Abstract: This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting...
arXiv:2603.03405v1 Announce Type: cross Abstract: The forward and reverse Kullback-Leibler (KL) divergences arise as limiting objectives in learning and inference yet induce markedly different inductive...
arXiv:2603.03411v1 Announce Type: cross Abstract: Observational causal discovery is only identifiable up to the Markov equivalence class. While interventions can reduce this ambiguity, in practice...
arXiv:2603.03476v1 Announce Type: cross Abstract: We propose a novel computational framework for analyzing electroencephalography (EEG) time series using methods from stringology, the study of efficient...
arXiv:2603.03493v1 Announce Type: cross Abstract: Benchmark rankings are routinely used to justify scientific claims about method quality in gene regulatory network (GRN) inference, yet the...