goodgoals/NextTokenSystem-Algebraic-Predictor

NextTokenSystem: Deterministic Algebraic Prediction Engine

Overview

This system is a novel approach to next-token prediction that replaces neural networks and stochastic sampling with deterministic algebraic transformations. It achieves high precision by mapping tokens to a numeric coordinate space and applying governing equations.

Core Algebraic Mechanism

The engine utilizes three primary equation types to model token relationships:

• Linear: $y = x + c$ (Constant shifts)
• Multiplicative: $y = a * x$ (Scale transformations)
• Quadratic: $y = x^2 + c$ (Non-linear jumps)

Temporal Dynamics

To ensure contextual coherence and adaptation, the system implements:

• Equation Memory: Successful equations accumulate strength over time.
• Decay: Unused rules gradually lose influence to prevent stale logic.
• Reuse Bias: Recently used equations receive a temporary bonus for style consistency.

Conflict Resolution

• Local Anchors: Specific token-to-token mappings are 'anchored' using optimized parameters, taking precedence over global rules.
• Global Rules: Weighted selection based on temporal dynamics and fit scores for general context.

Deterministic Precision

• Zero Neural Intervention: No transformers, embeddings, or backpropagation are used.
• 95%+ Precision Goal: Through adaptive coefficient scaling and symbolic back-search, the system targets near-perfect accuracy on structured datasets.