Abstract

TECHNICAL DISCLOSURE: RME-1 RESONANT MANIFOLD ENCODING

Document ID: RME-2026-001-DISC Publication Date: January 18, 2026 Field: Artificial Intelligence / Geometric Logic Constraints / Hallucination Mitigation

1. Abstract

This disclosure describes a system and method for constraining Large Language Model (LLM) generation through a 3D Geometric Manifold. Unlike traditional probabilistic token selection, the RME-1 protocol enforces structural integrity by measuring the "Reality Gap" between abstract symbols and grounded axioms, utilizing a "Literal-Floor Anchor" to prevent semantic drift.

2. Technical Field

The present disclosure relates generally to the fields of natural language processing (NLP) and machine learning, specifically focusing on deterministic governors that provide a secondary validation layer for generative outputs.

3. Background

Current generative models suffer from "Abstraction Drift," where the statistical likelihood of a word sequence outweighs its logical or physical validity. This results in hallucinations that are grammatically flawless but factually or structurally impossible. Existing solutions (RLHF, RAG) provide better data but do not address the underlying geometric instability of the semantic space.

4. Description of Innovation

A. The Theta-Shift Calibration

The core innovation is the modulation of Symbolic Density (SD) against Semantic Gravity (SG).

  • SD represents the technical/abstract complexity of the token.

  • SG represents the sensory-grounded/axiomatic weight. The relationship is defined by the Theta-Shift Equation: $$\theta = \arctan\left(\frac{SD}{SG}\right)$$

    The system dynamically forces$\theta$toward a "Resonance Zone" (30° to 60°). If the abstraction level ($SD$) grows too high without sufficient grounding ($SG$), the model triggers a compensatory repair.

B. The Literal-Floor Anchor (LFA)

A technical constraint where a specified percentage (Default: 60%) of the original "Input Ore" (the raw technical terms provided in a prompt or source document) must be preserved in the output. This prevents the model from "summarizing away" critical nuance or replacing precise jargon with vague synonyms.

C. Recursive Manifold Folding

A method of context management where previous interactions are not merely appended to a buffer but "folded" into the geometric manifold. Similar concepts are stored in high-density clusters, while divergent or contradictory logic is forced into orthogonal slots via a "Competitive Exclusion" update rule.

5. Exemplary Implementation

The system operates as an "Omni-Capability" layer. When a user provides intent, the Intent Vector (IV) determines the target$\theta$.

  1. Input Analysis: The system maps input tokens into the 3D Hilbert Space.

  2. Reality Gap Audit: Measures Euclidean distance from Hardened Axioms.

  3. Geometric Repair: If the Gap exceeds a threshold, the system injects structural metaphors to re-anchor the vector trajectory.

6. Claims of Novelty

  1. A method for mitigating AI hallucinations using a geometric resonance zone ($\theta$).

  2. A system for enforcing keyword retention through a Literal-Floor Anchor.

  3. The use of a 3D Semantic Manifold to quantify "Reality Gaps" in real-time generation.

Status: Defensive Publication / Prior Art Established. Authorized by: RME-1 Strategic Core.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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