arXiv:2410.08727v2 Announce Type: replace-cross Abstract: Diffusion models power leading generative AI, but when and how they memorize training data, especially on low-dimensional manifolds, remains unclear. We find memorization emerges gradually, not abruptly: as data become scarce, diffusion models experience a…
arXiv:2510.23914v2 Announce Type: replace Abstract: While Value Iteration (VI) is one of the most fundamental algorithms in Reinforcement Learning, its theoretical convergence guarantees still exhibit a persistent mismatch with empirical behavior. In the discounted-reward case, classical theory guarantees geometric convergence…
arXiv:2602.09375v3 Announce Type: replace Abstract: We propose LaPha, a method for training AlphaZero-like LLM agents in a Poincar’e latent space. Under LaPha, the search process can be visualized as a tree rooted at the prompt and growing outward from the…
arXiv:2603.10886v1 Announce Type: cross Abstract: We propose novel kernel-based tests for assessing the equivalence between distributions. Traditional goodness-of-fit testing is inappropriate for concluding the absence of distributional differences, because failure to reject the null hypothesis may simply be a result…
arXiv:2504.04371v3 Announce Type: replace Abstract: Traditional Support Vector Machine (SVM) classification is carried out by finding the max-margin classifier for the training data that divides the margin space into two equal sub-spaces. This study demonstrates limitations of performing Support Vector…
arXiv:2603.05621v2 Announce Type: replace-cross Abstract: Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low-level interfaces to high-level autonomous behaviour requires a complicated pipeline, whose components demand…
arXiv:2603.10512v1 Announce Type: cross Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning, and adaptive learning. However, resource-constrained environments pose critical challenges, as conventional deep learning methods heavily rely…
arXiv:2603.10053v1 Announce Type: new Abstract: The Pickup and Delivery Problem (PDP) is a fundamental and challenging variant of the Vehicle Routing Problem, characterized by tightly coupled pickup–delivery pairs, precedence constraints, and spatial layouts that often exhibit clustering. Existing deep reinforcement…
arXiv:2603.10055v1 Announce Type: new Abstract: Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: high-quality text is finite, it contains human biases, and it entangles…
arXiv:2603.10048v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) enhances generalization by minimizing the maximum training loss within a predefined neighborhood around the parameters. However, its practical implementation approximates this as gradient ascent(s) followed by applying the gradient at the ascent…
