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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.
Phase 1 establishes the ASRA Experience Engine: transition logging, hash-stable state IDs, effect-based action semantics inference, uncertainty-directed exploration, dead-end taboo, Swarm orchestration, and competition-grade execution fidelity. The article presents full theory and architecture; a companion Kaggle notebook deploys asra-v0.1-phase1 for ARC Prize 2026.
Evaluation protocol for measuring genuine learning across repeated ARC-AGI-3 episodes: full-game and single-level setups, primary metrics (action count, dead-end rate, visit redundancy), cross-run persistence requirements, and mapping to ASRA Phase 1–3 transition logging, semantics inference, and episodic memory.
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.
A step-by-step guide to how adaptive systems discover what actions mean from state transitions alone—without predefined action labels—using the ASRA transition-centric reasoning framework.