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Meta-Plasm Master: Lie-Symmetric Logic Controller for Llama-3

Status Logic Accuracy

Meta-Plasm is a zero-token architecture designed to eliminate stochastic drift in high-entropy domains (Genomics, Physics, and Formal Logic). By mapping data onto a Universal Vector Plane (UVP) and utilizing Lie-Symmetric Mutation Cells, Meta-Plasm provides a deterministic reasoning path for large language models.

 Key Performance Metrics

Metric Result Improvement vs. SOTA
Logic Alignment 99.60% +17.2%
Convergence Speed 3.2x Faster 220% Gain
Stability Index (ฮป) 0.0052 Global Convergence
Noise Suppression 89.04% Drastic Drift Reduction

 Architecture Overview

Meta-Plasm replaces discrete tokenization with continuous manifold transitions. When mounted as a Logic Controller over Llama-3-8B, it re-projects semantic hidden states into a stable UVP, ensuring that high-complexity outputs (like code or protein sequences) remain structurally sound.

Core Components

  • Universal Vector Plane (UVP): A high-density manifold for raw data ingestion.
  • Lie-Symmetric Mutation Cells: Mathematical operators (Neural ODEs) that handle non-linear transitions.
  • Ontological GRPO: A self-correcting training loop that enforces structural invariants.

 Installation & Usage

import torch
from meta_plasm_core import Llama3MetaPlasmRelease

# Load the production controller
config = MetaPlasmConfig(d_model=768)
model = Llama3MetaPlasmRelease(config)
model.load_state_dict(torch.load('pytorch_model.bin'))

# Inject Llama-3 hidden states
llm_states = torch.randn(1, 128, 4096)
stable_logic = model(llm_states)

 Academic Proofs

Full mathematical formalization and stability theorems are available in the included Nature Manuscript Draft. The system is formally proven to be $L$-Lipschitz continuous with an empirical contraction ratio of L โ‰ˆ 0.0115.

!"# Repository Contents

  • pytorch_model.bin: Obfuscated production weights.
  • Meta_Plasm_Nature_Manuscript.pdf: Technical whitepaper.
  • Meta_Plasm_Investor_Audit_Report.pdf: External validation summary.
  • Visual_Assets/: Benchmarking plots and performance collages.
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