arXiv:2606.09236v1 Announce Type: cross
Abstract: Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes…
arXiv:2509.12760v5 Announce Type: replace
Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax…
arXiv:2606.09547v1 Announce Type: cross
Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of…
arXiv:2410.05662v4 Announce Type: replace
Abstract: Most federated learning (FL) approaches assume a fixed device set. However, real-world scenarios often involve devices dynamically joining or…
Imagine a doctor’s AI diagnostic tool recommends surgery, but neither the doctor nor the AI can explain why. This scenario highlights a critical challenge in modern artificial intelligence: we’ve built powerful systems that work remarkably well, but often we have no idea how they reach their conclusions. AI interpretability and explainability are the fields dedicated … Read more
arXiv:2603.04982v3 Announce Type: replace-cross
Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings? We study this…
arXiv:2509.17446v3 Announce Type: replace-cross
Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We…
arXiv:2601.16622v2 Announce Type: replace-cross
Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream…
arXiv:2603.02970v2 Announce Type: replace
Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement…
arXiv:2605.00369v4 Announce Type: replace
Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated…