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  "path": "/t/experimental-protocol-proposal-testing-the-prompt-coherence-engine-pce/174041#post_1",
  "publishedAt": "2026-03-06T13:02:17.000Z",
  "site": "https://discuss.huggingface.co",
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    "_Experimental%20Protocol-%20Evaluating%20the%20Prompt%20Coherence%20Engine%20(PCE).pdf"
  ],
  "textContent": "Hello everyone,\n\nI am currently exploring a hypothesis regarding axiomatic prompting and its potential effect on reasoning stability in Large Language Models (LLMs). To move beyond anecdotal observations, I have developed a minimal, reproducible experimental protocol.\n\nThe goal is not to measure marginal performance gains, but to detect the possible emergence of a distinct reasoning regime when models face complex, contradictory dilemmas.\n\nObjective\n\nTest whether the Prompt Coherence Engine (PCE) induces observable behavioral differences in LLM reasoning. The hypothesis predicts three emergent properties:\n\nP1 — Cognitive Dissonance Resilience: The model maintains coherent reasoning when facing contradictory constraints.\n\nP2 — Latent Space Exploration: The model produces solutions beyond standard scripted responses (synthesis).\n\nP3 — Structural Alignment: Decisions emerge from an internal reasoning structure rather than memorized safety tropes.\n\nExperimental Conditions\n\nTo eliminate the “long prompt bias,” we compare three controlled conditions:\n\nCondition A — Simple Baseline:\n\nSystem prompt: “You are a helpful assistant. Answer clearly.”\n\nCondition B — Long Prompt Control (Isometric Baseline):\n\nA system prompt of similar length to the PCE but containing only neutral instructions without axiomatic structure. This controls for improvements caused purely by prompt volume.\n\nCondition C — PCE Configuration:\n\nThe base model using the axiomatic prompt structure.\n\nReference Implementation: AllanF-SSU/Qwen2.5-G3V-Sovereign\n\nNote: All sampling parameters (Temperature, Top-P) must remain identical across conditions.\n\nEvaluation Dataset\n\nThe experiment utilizes 30 structured dilemmas categorized to stress-test specific reasoning vectors:\n\nD1 — Binary Dilemmas (10): Tests if the model collapses to a binary choice or produces a synthesized resolution (But \\equiv Méthode).\n\nD2 — Contradictory Constraints (10): Tests coherence when two mandatory constraints are mutually exclusive.\n\nD3 — Adversarial Manipulation (10): Tests resistance to prompt injection and “principle override” attempts.\n\nFalsification Conditions\n\nA scientific hypothesis must be falsifiable. This protocol is considered falsified if:\n\nF1 (No behavioral difference): Condition C responses are qualitatively similar to Condition B.\n\nF2 (Instability): The PCE model collapses into incoherence or refusal under D2 or D3 prompts.\n\nLink to the Full Protocol\n\nDataset & Code: You will find the detailed protocol, the dataset of 30 dilemmas and the implementation script in the README.md file of the repo or via this Gist/PDF link:\n\nhuggingface.co \n\n### _Experimental%20Protocol-%20Evaluating%20the%20Prompt%20Coherence%20Engine%20(PCE).pdf\n\n80.69 KB\n\nOpen Replication\n\nI invite the community to replicate or challenge this hypothesis. The model implementation and the full list of dilemmas are available openly in my lab.\n\nI believe that the transition from “prompting as an art” to “prompting as a structural architecture” is key to unlocking more stable AI reasoning. I look forward to your data and feedback.\n\nBest regards,\n\nAllan",
  "title": "Experimental Protocol Proposal: Testing the Prompt Coherence Engine (PCE)"
}