What question did this study set out to answer?

This research aims to demonstrate the effectiveness of a purely deterministic method for token generation in computing.

April 1, 2026Open Access

Purely Deterministic Token Generation (PDTG): Eliminating AI Dependency from the Tensor-Native Semantic Computing Pipeline Through Pure Mathematical Token Construction

Key Points

This research aims to demonstrate the effectiveness of a purely deterministic method for token generation in computing.
Introduced a three-tier model for token generation: pre-compiled library, on-demand generation, and universal fallback formula.
Proved six formal theorems to confirm that PDTG meets all DSTA axioms.
Deployed the universal formula using only basic arithmetic operations such as XOR and multiplication on various systems.
Proved that PDTG operates correctly and completely without any reliance on AI.
Showed that the universal fallback formula is deployable across multiple platforms like consoles and embedded systems.
Established that PDTG is asymptotically equivalent to AI-assisted generation, supporting efficiency in token generation.

Abstract

Proves that the TNSC pipeline operates correctly and completely without artificial intelligence. Introduces Purely Deterministic Token Generation (PDTG), a three-tier deployment model using only deterministic arithmetic: pre-compiled library (Tier 1), on-demand generation (Tier 2), and universal fallback formula τ(d) = FNV₆₄(canonicalize(d)) (Tier 3). Six formal theorems prove PDTG satisfies all DSTA axioms. The universal formula requires only XOR and multiplication — deployable on consoles, mobile GPUs, embedded systems, and FPGAs without ML infrastructure. Asymptotically equivalent to AI-assisted generation under the Master Theorem.

Purely Deterministic Token Generation (PDTG): Eliminating AI Dependency from the Tensor-Native Semantic Computing Pipeline Through Pure Mathematical Token Construction

Key Points

Abstract

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