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This randomized trial reveals that structural recognition enhances AI's decision-making capabilities, suggesting improved optimization frameworks.
Structural Recognition above Metric Similarity (SRMS) presents a recognition-centered computational architecture for future AI. The repository argues that structural identity, critical deltas, decision boundaries, runtime activation, Runtime Invariants, structural confidence, and certified structural assets should be established before metric similarity performs ranking and optimization. It introduces the Base-Delta Asymmetry Principle, Recognition-Gated Cosine Scoring, Small but Decisive Structural Deltas, Structural Recognition as Runtime Intelligence, Structural Confidence Accumulation, Certified Structural Components, and the Structural Recognition Pipeline for Future AI. The central principle is: Recognition establishes the computational world; similarity optimizes inside that world.
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Tan et al. (2026) studied this question.
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