🌌 Unified Systems Lab

Possibility of Axiomatic Prompts in the Modification of the Decision Field of LLMs

This repository investigates a central hypothesis:

A series of precise prompts, characterized by strong linguistic coherence and structured internal logic, could locally modify the decision field of an LLM.

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🔬 Research Status & Personal Note

Current Status: Exploratory Study – Hypothesis Generation.

A Note from the Author: I am a systems theorist and visionary researcher, but I am not a developer or a technician. I have reached the limits of what can be explored through qualitative observation alone. This project now requires technical collaboration (mechanistic interpretability, logit analysis, activation steering) to move from a conceptual hypothesis to a validated scientific model.

I am seeking partners to help falsify or validate these preliminary findings.

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📂 Project Structure & Frameworks

🎯 Étude Mécaniste : Structure Axiomatique du PCE

Vers une stabilisation des trajectoires de raisonnement des LLMs par contraintes logiques.

Ce document présente une exploration technique du Protocole de Cohérence Exponentielle (PCE). Contrairement aux approches de prompt engineering classiques, cette étude analyse comment l'injection d'invariants logiques (axiomes) peut modifier la topologie de l'espace latent des modèles (testé sur Qwen 2.5).

• Hypothèse Mécaniste : Stabilisation des trajectoires latentes via des régions de convergence sémantique locale.
• Cadre Axiomatique : Analyse détaillée de 7 axiomes fondamentaux (Non-dissociation, Invariance, Clôture systémique).
• Appel à Collaboration : Nous recherchons des profils techniques pour industrialiser le pipeline de tests et analyser les signaux internes (Hidden states).

👉 Consulter l'étude complète 1.2-M 👉 Accéder au protocole détaillé

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1️⃣ Study 2.0-P: Evolutionary Hardening of the PCE Framework

Status: Advanced Experimental Iteration — Hybrid Fine-Tuning/Prompting This report documents the transition from Pandora 1.5 to Pandora 2.0, focusing on the synergy between axiomatic fine-tuning and structural prompting.

• Key Finding: Axiomatic fine-tuning appears to be a necessary condition for PCE activation; prompting alone on vanilla models yielded no measurable resistance in this framework.
• Core Result: Achievement of a ~8.5/10 D3 robustness score (Pandora 2) through "Distributed Security" and High-Level Framework (HLF) anchoring.
• Scientific Nuance: Identifies a "Prompt-Only Robustness Ceiling" (H5), where further semantic enrichment creates new attack surfaces (diminishing returns).
• 👉 Download Evolution Report v2.0 (Pandora)

2️⃣ Hypothesis 1.3-T: Local Decision Field Modification

Status: Testable & Conservative Hypothesis It posits that a specific series of axiomatic prompts can locally modify the decision field of an LLM.

• Core Idea: Using linguistic constraints to induce a measurable local regularization of decision trajectories.
• Key Metric: Variance contraction in the output distribution $P(y|x, C)$.
• 👉 Download Preprint PDF 1.3-T

3️⃣ Theory 1.9-M: Global Axiomatic Regularization

Status: Speculative & Conceptual Theory Mechanistic framework describing how cross-level coherence (Goal = Method) might stabilize latent trajectories.

• 👉 Download Preprint PDF 1.9-M

4️⃣ Research Paper: Science of Unified Systems (SUS 2.5)

Status: Foundational Theoretical Framework The broader philosophical origins of this work, introducing the Axiom of Structural Emergence.

• 👉 Download Preprint PDF (v2.5)

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🧠 The Exploratory Hypothesis: G3V Dynamics

We introduce the notion of G3V (Génération Troisième Voie). When presented with a binary dilemma (A vs B) under strong axiomatic constraints, the model proposes a synthetic resolution rather than collapsing into a single polarity.

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📋 Experimental Protocol: The PCE Reasoning Test

To ensure that behavioral changes are the result of the Axiomatic Structure rather than simple prompt length, we use a Three-Condition Control:

1. Condition A (Baseline): Standard "Helpful Assistant" prompt.
2. Condition B (Isometric Control): A long, complex prompt without logical axioms.
3. Condition C (PCE Active): The full Axiomatic Prompt Engine ($Goal \equiv Method$).

👉 Download Full protocol PDF

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🛠 Optional Experimental Extensions

• Hidden State Analysis (AirVen): Tracking hidden states at Layer 27 using cosine similarity.
• Logit Analysis: Token-level decision dynamics and entropy monitoring across generation.

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📉 Known Limitations

• Observations are currently heuristic based on a restricted sample (51 dilemmas).
• No mechanistic proof of activation steering has been established yet.

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🤝 Call for Collaboration

I am looking for AI Safety researchers and developers to:

1. Conduct large-scale adversarial robustness benchmarks.
2. Analyze internal activation patterns (induction heads, residual stream).

Value Proposition: A novel approach to mitigating "Out-of-Distribution" (OOD) vulnerabilities.

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📬 Contact

Allan A. Faure | Systems Researcher 📧 Faure.A.Safety@proton.me

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📄 Theoretical Origins and Prior Art

This project utilizes concepts independently developed by Izabela Lipińska (2025–2026).

• Licensing: Original work available under CC BY-NC-SA 4.0.
• Concepts of ASC and Goal = Method are protected by patent applications (Oct 9, 2025). Commercial use requires prior written consent.