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Comparative analysis of Buchanan, Pai, Wang, and Ma's representation-learning textbook (arXiv:2606.06624) and the Adaptive Scientific Reasoning Architecture (ASRA). Buchanan–Ma formalizes compressive memory and white-box deep representations; ASRA formalizes intervention loops, causal semantics, goal hypotheses, and experiment design. The programs are complementary: memory theory vs scientific reasoning under uncertainty.
Intelligence is framed not as optimization alone but as the search for increasingly useful representations of reality. Before learning can succeed, a system must discover appropriate state spaces, action semantics, evaluation criteria, and ontologies. This concept paper argues that prompts, benchmarks, world models, and ontologies are central substrates of intelligence, and introduces ASRA as a representation-first framework that infers semantic operators from observed transitions before constructing causal world models.