Metadata-Version: 2.4
Name: apophatic_engine
Version: 0.1.0
Summary: Apophatic noise-subtraction filter (¬X) for LLMs.
Author: xerx593
Project-URL: Homepage, https://github.com/xerx593/apophatic-engine
Project-URL: Theory Framework, https://github.com/xerx593/nicht-theory
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: The Unlicense (Unlicense)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20.0
Dynamic: license-file

# Apophatic Engine (`apophatic-engine`)

> *Apophatic Noise Subtraction ($\neg X$) for Neural Inference and System Optimization.*

The **Apophatic Engine** implements token-by-token noise subtraction ($\neg X$) derived from the **Nicht-Theory** theoretical framework. Instead of maximizing speculative probabilities ($1$-logic), the filter penalizes assertion pressure ($P_A$) and relaxes high-entropy tokens directly into the **Sufficient Invariant Baseline ($B_0$)**.

## Theoretical Grounding
This repository provides the operational code implementation for Paper IV (*Apophatic Inference Engine*) of the [Nicht-Theory Framework](https://github.com/xerx593/nicht-theory).

## Quickstart
```python
from apophatic_filter import ApophaticEngine
import numpy as np

engine = ApophaticEngine(sigma_threshold=0.40)
logits = np.array([1.2, 1.1, 1.3, 1.0])  # High-noise logits

result = engine.apply_apophatic_filter(logits, step=1)
print(result)
# Output: {'status': 'RELAX_TO_BASELINE', 'output_token': '[B_0: SILENCE / UNASSERTED]'}
