A new paper introduces novel algorithms for sequence prediction based on stringology, aiming to bridge theoretical agent foundations with practical algorithms. The research focuses on measures like the size of straight-line programs and minimal automata to predict sequences efficiently. This work represents a significant step in compositional learning theory, potentially leading to more realistic models of agents that use Occam's razor, offering a new mathematical model for deep learning's generalization power, or even providing a practical alternative to deep learning for building AI. AI
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RANK_REASON This is a research paper introducing novel algorithms and theoretical concepts in sequence prediction.