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Interim consolidated evaluation report for ASRA: three tracks (gateway plumbing, competition scores, repeated-run learning), Phase 1–2 Kaggle results, Phase 2 Original ARC benchmark summary, agent version ladder, and roadmap to v1 after Stage 1 gateway migration completes.
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.