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Phase 4 adds the Semantics and Causal Inference Engine: semantic signatures, causal transition models, hypothesis confirm/refute, counterfactual queries, and epistemic uncertainty over (state, action) pairs—building on Phases 1–3. The article presents theory and architecture; a companion Kaggle notebook deploys CausalSemanticsEngine hints for ARC Prize 2026.
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.
Scientific intelligence increasingly depends on systems that reason from interventions rather than merely fit observations. This review synthesizes conceptual foundations from model-based reinforcement learning, active inference, causal inference, information theory, and perturbation biology into a unified architecture-level view of adaptive scientific reasoning under uncertainty.