arXiv:2603.25839v1 Announce Type: new
Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing superv...
Pure and Physics-Guided Deep Learning Solutions for Spatio-Temporal Groundwater Level Prediction at Arbitrary Locations
arXiv:2603.25779v1 Announce Type: new
Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challenging task. Theory-based models have been the cornerstone of scientific understanding. However, their...
Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
arXiv:2603.25771v1 Announce Type: new
Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the...
As artificial intelligence becomes central to national security, experts grapple with a technology that remains unpredictable, unregulated, and increasingly powerful.
Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Tex...
If the last wave of AI felt like hiring a very smart intern, this one feels more like managing an entire organization that never sleeps (and occasionally argues with itself).
Safe Reinforcement Learning with Preference-based Constraint Inference
arXiv:2603.23565v1 Announce Type: new
Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restric...
Drop-In Perceptual Optimization for 3D Gaussian Splatting
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a di...
Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations
arXiv:2603.22345v1 Announce Type: new
Abstract: Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g., text, audio, images, etc.). Existing studies have shown that GCN can...
Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report
arXiv:2603.22306v1 Announce Type: new
Abstract: Affective judgment in real interaction is rarely a purely local prediction problem. Emotional meaning often depends on prior trajectory, accumulated context, and multimodal evidence that may be weak, noisy, or incomplete at the current moment. Althoug...
Stanford computer scientist James Zou is exploring how AI can accelerate scientific research and peer review. His finding: AI excels at spotting gaps, but judgment calls still need humans.
Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration
arXiv:2603.20297v1 Announce Type: new
Abstract: Accurate calibration is essential for instruments whose measurements must remain traceable, reliable, and compliant over long operating periods. Fixed-interval programs are easy to administer, but they ignore that instruments drift at different rates ...
SafetyPairs: Isolating Safety Critical Image Features with Counterfactual Image Generation
This paper was accepted at the Principled Design for Trustworthy AI — Interpretability, Robustness, and Safety across Modalities Workshop at ICLR 2026.
What exactly makes a particular image unsafe? Systematically differentiating between benign and problematic images is a challenging problem, as subt...
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
arXiv:2603.19295v1 Announce Type: new
Abstract: Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to the definition of positive pairs in contrastive learning. To address this, we propose a subtype-gu...
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
arXiv:2603.18032v1 Announce Type: new
Abstract: Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures,...
arXiv:2603.17063v1 Announce Type: new
Abstract: Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways.
First, we prove that every sigmoid transformer wi...
AI-powered robot learns how to harvest tomatoes more efficiently
A new tomato-picking robot is learning to think before it acts. Instead of simply identifying ripe fruit, it predicts how easy each tomato will be to harvest and adjusts its approach accordingly. This smarter strategy boosted success rates to 81%, with the robot even switching angles when needed. Th...
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new
Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex...