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  "path": "/t/research-proposal-v1-3-t-local-decision-field-modification-in-llms-via-axiomatic-prompting/173974#post_1",
  "publishedAt": "2026-03-03T13:45:29.000Z",
  "site": "https://discuss.huggingface.co",
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    "Axiomatic_Qween_1.3-T_Faure_Preprint.pdf · AllanF-SSU/Research-Papers at main"
  ],
  "textContent": "Hello everyone,\n\nI would like to share a significant change in direction regarding my recent work. Rather than pursuing a highly speculative or conceptual formulation, I am now focusing on a more modest and testable version of my hypothesis: v1.3-T (T for Testable).\n\nThe goal is simple: move step by step, reduce speculation, and concentrate on measurable effects.\n\nCore Hypothesis (Testable Version)\n\n“A coherent series of axiomatic prompts, characterized by strong internal linguistic logic and structured constraints, could locally modify the decision field of a Large Language Model within a given conversational horizon.”\n\nBy “local decision field,” I mean the distribution of interpretative trajectories accessible to the model in a specific context.\n\nThis hypothesis does NOT claim:\n\n  * Any modification of weights.\n  * Any global invariance.\n  * Any permanent attractor.\n  * Any proof of global regulation.\n\n\n\nIt only proposes the possibility of a stable contextual modulation that can be empirically tested.\n\nTheoretical Background\n\nCurrent LLMs optimize a local conditional distribution:\n\nP(x_{t+1} | x_{1:t})\n\nThis local optimization does not guarantee global trajectory stability. Known consequences include:\n\nVulnerabilities: Jailbreak, prompt injection, behavioral/alignment drift.\n\nInconsistencies: Long-horizon contradictions.\n\nExisting approaches (RLHF, Constitutional AI, CoT, etc.) mainly operate through local constraints or external heuristics. They improve local reasoning but do not explicitly attempt to restructure the decision topology itself.\n\nProposed Direction: The A-Frame\n\nThe v1.3-T version explores whether a structured axiomatic prompt architecture (A-Frame) can:\n\n  * Reduce local variance under reformulation.\n  * Increase contextual resilience under perturbation.\n\n\n\nIncrease the frequency of G3V (Third Way Generation): Defined operationally as the model generating a constrained integrative resolution when facing a binary dilemma (A vs B), rather than collapsing immediately to one polarity.\n\nNote: This is viewed as an emergent pattern under constraints and does not imply any form of machine consciousness.\n\nExploratory Observations\n\nUsing an exploratory benchmark (51 diverse dilemmas) on Qwen 2.5 1.5B (open-source, no weight modification), comparing a standard prompt vs. 3 axioms vs. 6 axioms, we observed:\n\n  * Improved coherence progression: (3 axioms partial stabilization).\n  * Zero explicit contradictions: In the 6-axiom configuration within this limited sample.\n  * Increased refusal stability and apparent OOD (Out-Of-Distribution) robustness.\n  * Non-opportunistic third-way responses.\n\n\n\nImportant Limitations:\n\n  * These observations are hypothesis-generating, not a validation.\n  * Small sample size & heuristic metrics.\n  * No isometric length control (prompt length as a confounder).\n  * No activation/logit analysis yet.\n\n\n\nCall for Collaboration\n\nI am now seeking collaboration with researchers and developers to increase methodological rigor:\n\nDesign proper isometric baselines (controlling for prompt length).\n\n  * Measure decision variance formally.\n  * Test cross-model robustness (scaling laws).\n\n\n\nAnalyze logits or internal activations (mechanistic interpretability).\n\nThe aim is to move from speculative framing toward controlled empirical validation. I would greatly appreciate feedback, criticism, or collaboration from anyone interested in alignment, robustness, or prompt-level regularization.\n\nThank you for reading.\n\nAllan A. Faure\n\nDownload Preprint PDF 1.3-T : Axiomatic_Qween_1.3-T_Faure_Preprint.pdf · AllanF-SSU/Research-Papers at main",
  "title": "[Research Proposal] v1.3-T: Local Decision Field Modification in LLMs via Axiomatic Prompting"
}