Late Time Transition

"Late-time transition" refers to phenomena where systems exhibit a significant change in behavior after an extended period of operation or evolution. Current research focuses on understanding these transitions across diverse fields, employing models ranging from neural networks and Markov chains to deep reinforcement learning algorithms, often analyzing the impact of factors like data characteristics (e.g., entanglement, complexity) and model architecture (e.g., width, depth, bottleneck layers). These studies aim to improve the predictability and control of such transitions, with implications for areas like AI safety (AGI scenarios), cosmological modeling, IoT security, and optimization of logistics systems.