What question did this study set out to answer?

This research aims to explore throughput convergence in various domains, assessing elements like information per serial event.

July 5, 2026Open Access

The Throughput Basin: Cross-Substrate Convergence and Decomposition of Serial Decoding Throughput

Puntos clave

This research aims to explore throughput convergence in various domains, assessing elements like information per serial event.
Examined throughput across six domains: genetics, AI, human cognition, music, language, and engineered encodings.
Analyzed 31 systems with vocabulary sizes up to 64,000 and proposed the Throughput Basin Decomposition.
Conducted evolutionary simulations and a permutation test to assess statistical significance based on AI data inclusion.
Found effective information per serial event within a 3–6 bit range.
Converged evolutionary simulations yielded K values between 19–30 and bit values of approximately 4.1–4.6.
Statistical significance in convergence was dependent on including AI data in the analysis.

Resumen

Building on the rate-distortion floor established in a companion paper, we examine throughput convergence across six domains: genetics, artificial intelligence, human cognition, music, language, and engineered encodings. Across 31 systems with vocabulary sizes spanning 64, 000×, effective information per serial event occupies a 3–6 bit band. We propose the Throughput Basin Decomposition: Iₑff = RM (ε) + Δₛ + ξ. Three independent evolutionary simulations converge to K ≈ 19–30 and ~4. 1–4. 6 bits. A permutation test reveals that cross-substrate statistical significance depends on AI data inclusion — a critical caveat we report transparently.

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