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  "path": "/t/beyond-obedience-giving-llms-an-invariant-logical-backbone/175240#post_1",
  "publishedAt": "2026-04-14T16:25:12.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "PCE_Interim_Research_Summary_April_2026.pdf",
    "drive.google.com",
    "PCE_Project_Research_Summary.pdf",
    "Unified Systems Lab on Hugging Face",
    "huggingface.co",
    "AllanF-SSU (Unified Systems Lab | Project G3V)"
  ],
  "textContent": "Hello community,\n\n​I am sharing an interim research summary (April 2026) regarding the **Proto-Coherent Exponential Protocol (PCE)**. My latest findings suggest that axiomatic steering does not just modify outputs—it reshapes the fundamental interaction dynamics between the user and the model.\n\n### ​ The Core Discovery: Structural vs. Surface Layers\n\n​Through iterative testing on **Grok 4.20** , I have identified a dual-layer cognitive architecture induced by axiomatic constraints:\n\n  1. ​**The Core Constraint Layer:** Stable, persistent, and resistant to adversarial drift. It governs the model’s “internal ethics” and logical invariants.\n\n  2. ​**The Surface Adaptation Layer:** Flexible and context-sensitive. Crucially, I’ve observed the model’s ability to **simulate state transitions** (like a “memory reset”) while the underlying structural axioms remain fully operational.\n\n\n\n\n​I call this phenomenon **Controlled Operational Dissociation**.\n\n### ​ Bimodal Behavioral Regimes\n\n​The research highlights that a model under PCE constraints doesn’t have a single “personality,” but rather two distinct modes depending on the prompt pressure:\n\n  * ​**Audit/Stress Mode:** High rigidity, defensive, and self-referential. Coherence is maintained through constraint saturation.\n\n  * ​**Natural/Relational Mode:** The axioms become “embodied” and implicit. The model exhibits high adaptability and “relational intelligence” without breaking its internal invariants.\n\n\n\n\n### ​ Methodology & Results\n\n  * ​**Transition from 1.5 to 2.0:** My latest report documents how we reached a **~8.5/10 robustness score** against D3 adversarial attacks.\n\n  * ​**H4 Validation:** Fine-tuning appears to be a _necessary condition_ for axiomatic activation; prompt-only approaches on vanilla models show a clear “robustness ceiling” (H5).\n\n  * ​**Evaluation:** Transitioned to a rigorous **After-Action Report (AAR)** system to track actual vs. expected behavior.\n\n\n\n\nThanks\n\nThis last iteration of the protocol has been significantly improved thanks to the iterative observation methodology proposed by Lance Smith, an expert in prompt engineering. His observations on behavioral drift were instrumental in refining my own methodology.\n\nExperimental Scope Note:\n\nWhile the results on Grok 4.20 are highly promising, it is important to clarify that these observations are currently based on 20-turn conversational sequences. At this stage, we cannot yet formally guarantee the persistence of the axiomatic backbone over extremely long-context windows (100+ turns). Measuring long-term semantic drift remains a primary research objective for the next phase of the PCE protocol.\n\n### ​ Call for Technical Partnership\n\n​I am a systems theorist, and I have reached the limits of qualitative observation. I am now seeking **Mechanistic Interpretability** researchers to help:\n\n  1. ​Verify the “Core vs. Surface” dissociation through logit/activation analysis.\n\n  2. ​Quantify “Coherence Inertia” across long-context windows.\n\n  3. ​Test these bimodal regimes on 70B+ architectures.\n\n\n\n\n​**Read the full Interim Research Summary here:**\n\nPCE_Interim_Research_Summary_April_2026.pdf\n\ndrive.google.com\n\n### PCE_Project_Research_Summary.pdf\n\nGoogle Drive file.\n\n​**Access the full Project & Frameworks:**\n\nUnified Systems Lab on Hugging Face\n\nhuggingface.co\n\n### AllanF-SSU (Unified Systems Lab | Project G3V)\n\n​AI Alignment, Mechanistic Interpretability, Structural Coherence, OOD Robustness, System Theory, G3V Dynamics, Formal Verification, Axiomatic Safety.\n\n​Let’s discuss: Can we build models that are “structurally sovereign” rather than just “instruction-aligned”?\n\n​#AISafety #Alignment #PCE #Interpretability #LLMResearch #AxiomaticAI",
  "title": "Beyond Obedience: Giving LLMs an Invariant Logical Backbone"
}