I am archiving the current milestone for the GERS (Geometric Embedding Resonance Steering) framework.

This system implements real-time manifold intervention on Large Language Models, replacing traditional fine-tuning with geometric operators on hidden states.

Core Architecture

The current iteration (v4.1) operates on a four-layer decoupled architecture:

1. Engine: Calculation backend (hook injection).
2. Steerer: Manifold operator execution (Affine2/Group Actions).
3. State: Atomic experiment management.
4. Interface: Interactive CLI.

Experimental Results

On a quantized 7B model (int8), GERS achieved a significant performance jump in few-shot intent classification tasks:

• Baseline: 66% accuracy.
• GERS + Linear Probe Synergy: 97% accuracy.

This confirms the hypothesis that high-precision control is achievable via affine transformations on the semantic manifold, specifically within the 45%-65% depth range ("Gold Zone").

Proof of Existence

To establish priority for the technical mechanisms described in GERS Technical Whitepaper v4.1 (specifically the Affine2 operator and Resonance Scanning algorithm) without public disclosure, I am publishing the SHA-256 hash of the document below.

Document: gers_whitepaper_v4.1.pdf
Date: 2026-02-02
SHA-256: c1c7f9ae665af9ffd151ec3e3559a4668d1e14c2eef46d8aba11015d5f246e33