Constraints That Compute: A Unified Framework for Efficient Intelligence
The paper presents a domain-agnostic framework that achieves computational efficiency by transforming systems into their intrinsic, dimensionless geometries rather than relying on large-scale processing. It demonstrates that intelligent and adaptive behavior can emerge from strict internal constraints across diverse fields such as mathematics, physics, game dynamics, and artificial intelligence. The approach is validated through applications including prime harmonics, an emergent chess engine, jet tagging in particle physics, and latent reasoning in AI.
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Published April 29, 2026 | Version v1 Publication Open Constraints That Compute: A Unified Framework for Efficient Intelligence from Prime Harmonics to Latent Reasoning Authors/Creators Concas, Massimiliano (Researcher)1 Show affiliations 1. Ciber Fabbrica Description This paper introduces a domain-agnostic framework that replaces brute-force computation with structural efficiency by translating systems into their intrinsic, dimensionless geometries. Validated across pure mathematics (prime harmonics), dynamical systems (a zero-knowledge emergent chess engine), high-energy physics (scale-invariant jet tagging), and artificial intelligence (Relational-CoT for latent reasoning), the research demonstrates a unified principle: intelligent, adaptive, and highly efficient behavior emerges from…
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