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