| # NextTokenSystem: Deterministic Algebraic Prediction Engine |
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| ## 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. |
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| ## 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) |
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| ## 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. |
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| ## 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. |
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| ## 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. |
