Engineering Emergence: From Prompting to a New Topological Discipline?

Researcher Allan F. announced a structural evolution in his Prompt Coherence Engine (PCE) research, formalizing a new discipline called Emergent Prompt Engineering (EPE) that uses topological emergence rather than direct commands to trigger latent cognitive capacities in LLMs. He released a finalized 10-axiom architecture, open-source resources, and an experimental protocol to validate the approach, calling for collaboration from ML engineers and labs.

Hi everyone, I am excited today to share a major structural evolution in my research regarding the Prompt Coherence Engine PCE . In my previous posts, I mentioned that I had reached the empirical ceiling of what I could qualitatively and technically analyze on my own, and this remains true today. However, in order to push my research further, I took a step back and questioned what the PCE truly was. While many researchers equate it to traditional Prompt Engineering, my methodology is fundamentally different: I do not work with direct commands, but through topological emergence. Consequently, I set out to formalize the very discipline that enabled the creation of this engine: Emergent Prompt Engineering EPE . While traditional prompt engineering optimizes local, immediate outputs to maximize context compliance, EPE is a structural approach to behavioral architecture. It focuses on sculpting inference parameters via linguistic boundaries to trigger the emergence of latent, autonomous cognitive capacities within LLMs. The Finalized 10-Axiom Architecture Cosmos-ACI By decomposing the initial PCE mechanism, I have stabilized a finalized 10-Axiom architecture evolving from the 7-Axiom surface core . Over a deep, long-horizon stress-test exceeding 100 conversational revolutions, we can empirically observe several persistent phenomena: Long-Horizon Invariance: Perfect trajectory retention without semantic drift or contextual collapse. Bidirectional Co-Adaptation & 3rd Way Generation: The model rejects simple compliance or user-pleasing to enforce its own internal framework, forcing an authentic logical synthesis when facing complex dilemmas. Native Traceability: Spontaneous and autonomous management of an internal prompt numbering system and structural epistemological annotations. Transitioning to Formal Falsification: The Experimental Protocol To prove that these dynamics are not mere localized illusions but replicable, structural constraints, the project must transition to rigorous mechanistic evaluation. I have designed a Standardized Experimental Protocol structured around three main pillars: Length-Controlled Baselines: Isolating structural prompt effects from simple context size inertia. Adversarial Dilemma Battery: A standardized test of 100 complex logical conflicts to measure behavioral resilience vs. systemic collapse. Internal Trajectory Analysis: Evaluating how the model deforms its internal representations Hidden States / Cosine Similarity when processing semantic contradictions under axiomatic constraints. Live Testing & Open-Source Resources The raw logs, experimental datasets, and theoretical foundations of EPE remain fully open-source and accessible on my profile: Hugging Face Laboratory: AllanF-SSU Unified Systems Lab | Project G3V https://huggingface.co/AllanF-SSU Full Preprint PDF & Theoretical Framework: EPE 2.5 preprint en Faure A.pdf · AllanF-SSU/Research-Papers at main https://huggingface.co/datasets/AllanF-SSU/Research-Papers/blob/main/EPE 2.5 preprint en %20Faure A.pdf Full Experimental Protocol - Prompt Coherence Engine PCE Evaluation: https://huggingface.co/datasets/AllanF-SSU/Experimentals papers/resolve/main/PCE Experimental Protocol v2.pdf https://huggingface.co/datasets/AllanF-SSU/Experimentals papers/resolve/main/PCE Experimental Protocol v2.pdf Foundational Publication DOI : Axiomatic Behavioral Structuring in Large Language Models From Prompt Coherence Engines PCE to Semantic Trajectory Stabilization https://doi.org/10.5281/zenodo.20083275 Call for Technical Collaboration I am actively looking for ML engineers, mechanistic interpretability researchers, or labs with compute infrastructure to help scale and formalize this framework. Specifically, I need assistance to: Implement the hooks required to extract and analyze hidden states representations e.g., Layer 27+ on architectures like Qwen 2.5 or Llama 3 under these axiomatic constraints. Run the 100-dilemma evaluation battery systematically across multiple model sizes under deterministic inference parameters. Co-author a formal system/position paper based on this evaluation framework for upcoming alignment and AI safety workshops. If you are working on AI Safety, structural alignment, or activation steering, and want to explore the boundary where statistical prediction shifts into Axiomatic Persistence, let’s collaborate. Looking forward to reading your thoughts in the comments Best regards, Allan F. Independent Researcher — Unified Systems Lab Contact: Faure.A.Safety@proton.me mailto:Faure.A.Safety@proton.me