When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
arXiv:2606.19827v1 Announce Type: cross Abstract: Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored…
Reinforcement Learning Foundation Models Should Already Be A Thing
arXiv:2606.18812v2 Announce Type: replace-cross Abstract: Foundation models for language and vision are powered by internet-scale data, while structured domains such…
NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation
arXiv:2606.18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has…
Enhancing Pathological VLMs with Cross-scale Reasoning
arXiv:2606.17412v2 Announce Type: replace-cross Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at…
Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration
arXiv:2606.19042v1 Announce Type: cross Abstract: In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a…
A Hybrid LSTM–Vision Transformer Architecture for Predicting HRRR Forecast Errors
arXiv:2606.19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary…
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
arXiv:2606.16214v2 Announce Type: replace-cross Abstract: Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications.…