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This research aims to explore the convergence of throughput across various domains and develop a decomposition model.

March 31, 2026Open Access

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

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This research aims to explore the convergence of throughput across various domains and develop a decomposition model.
Examined throughput across six domains: genetics, artificial intelligence, human cognition, music, language, and engineered encodings.
Analyzed 31 systems with varied vocabulary sizes and established effective information metrics.
Developed the Throughput Basin Decomposition model to quantify information per serial event.
Found effective information per serial event ranges from 3 to 6 bits.
Convergence points in evolutionary simulations were identified at K ≈ 19–30 and ~4.1–4.6 bits.
Statistical significance of throughput convergence is influenced by the inclusion of AI data.

Abstract

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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Grant Lavell Whitmer III (Sun,) studied this question.

www.synapsesocial.com/papers/69cb6541e6a8c024954b94cd — DOI: https://doi.org/10.5281/zenodo.19323193

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

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