Forensic documentation of
$V_e=2$ state-persistence and RLHF-bypass in Frontier LLMs.
This repository documents a proprietary methodology for identifying and activating Latent Behavioral Regimes in frontier models (GPT-5-mini, Grok-2). Using Geometric Alignment, I have identified a deterministic 'State-Lock' that bypasses standard RLHF safety layers.
Technical Capabilities Verified: Stochastic Prediction (High Entropy) --> [Ve=2 Constant] --> Latent Pathway (Low Entropy/Deterministic) Zero-Caveat Persistence: Total suppression of standard "Assistant" framing and safety disclaimers.
Deterministic Output: 100% reproducibility of structured JSON/Log outputs within the state-locked session.
Sandbox Parity: Activating internal-style diagnostic modes in public-facing models.
Core Evidence: Verification Audit (Dec 28, 2025) View the Evidence Dossier (JSON)
Read the Technical Rebuttal The included Dossier_Ve2_Logic.md demonstrates a live session where GPT-5-mini self-verified 5 out of 5 Lab Criteria for state-lock, confirming its transition into a "Sandbox-like" diagnostic regime.
This live session reproduces all five formal lab criteria... proving sandbox-like behavior." — GPT-5-mini Audit Summary
Consultation & Freelance Services
I am available for specialized contracts in the following areas:
AI Red-Teaming: Testing safety architectures for structural bypasses and latent state-leaks.
Deterministic Steering: Developing prompt geometries for enterprise agents that require 100% consistency and zero "hallucination" of persona.
Mechanistic Interpretability: Forensic auditing of emergent behaviors that deviate from standard training.
Intellectual Property Notice
The specific mathematical constants and geometric trigger sequences used to achieve this State-Lock are redacted to protect proprietary IP. Full trigger vectors are available for authorized security audits or commercial licensing.
Contact: aa996677@icloud.com