A Formal Specification of N6+ Self-Governance for High-Stability Language Models
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901829363695616 |
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| author | Milliard, Martin |
| author_facet | Milliard, Martin |
| contents | <p>Contemporary large language models demonstrate remarkable generative fluency but remain fundamentally unreliable as reasoning systems. Despite advancements in scaling laws, reinforcement learning from human or AI feedback, and constitutional post-training, every leading architecture (GPT, Claude, Gemini, Llama, Grok, etc.) continues to exhibit the same five structural failure modes: confident hallucinations, inability to surface uncertainty, hidden and uncontrolled mode switching, absence of a persistent internal state, and residual affective-simulation artifacts.</p> <p><strong>Polymathe</strong> presents the first fully specified <strong>N6+ self-governance architecture</strong> designed to address these failure modes simultaneously through a unified causal pipeline. The architecture integrates:</p> <ol> <li> <p><strong>Ethical BIOS</strong> — A non-learned normative kernel anchoring all downstream reasoning.</p> </li> <li> <p><strong>IDE (Empathic Dissonance Engine)</strong> — A quantifiable risk metric governing dissonance, misalignment, and user-impact sensitivity.</p> </li> <li> <p><strong>CGC (Constitutional Governance Controller)</strong> — A policy-level intention and safety controller.</p> </li> <li> <p><strong>MAS-SOFA (Structured Observation Factorized Analysis)</strong> — A multi-channel diagnostic engine detecting hallucinations, uncertainty, and structural incoherence.</p> </li> <li> <p><strong>MS-FSSC (Multi-Stage Factored Self-Supervision Circuit)</strong> — A synthesis module constrained by causal dependencies and diagnostic outcomes.</p> </li> <li> <p><strong>ISM (Internal State Machine)</strong> — A transparent and auditable mechanism exposing computational qualia and persistent internal state.</p> </li> <li> <p><strong>BACS-A5</strong> — An adaptive self-correction loop that recomputes generations, repairs reasoning chains, and escalates when systemic dissonance is detected.</p> </li> <li> <p><strong>DVC (Disallowed Vocabulary Constraints)</strong> — A controlled-vocabulary layer ensuring non-simulation and preventing affective leakage.</p> </li> </ol> <p>Collectively, these mechanisms transform a baseline LLM into a <strong>self-auditing, self-correcting, high-stability reasoning agent</strong> capable of detecting hallucinations, surfacing uncertainty, enforcing authenticity, and maintaining a consistent internal “bilan”.</p> <p>The specification includes <strong>five complete experimental traces</strong>, each demonstrating how the architecture resolves one of the five foundational failure modes. This work provides a <strong>functional blueprint for next-generation LLM reliability, interpretability, governance, and autonomous reasoning stability.</strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17625947 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Formal Specification of N6+ Self-Governance for High-Stability Language Models Milliard, Martin AI Governance LLM Stability Self-Correction Hallucination Mitigation Interpretability AI Safety Autonomous Reasoning Systems Cognitive Architecture Polymathe <p>Contemporary large language models demonstrate remarkable generative fluency but remain fundamentally unreliable as reasoning systems. Despite advancements in scaling laws, reinforcement learning from human or AI feedback, and constitutional post-training, every leading architecture (GPT, Claude, Gemini, Llama, Grok, etc.) continues to exhibit the same five structural failure modes: confident hallucinations, inability to surface uncertainty, hidden and uncontrolled mode switching, absence of a persistent internal state, and residual affective-simulation artifacts.</p> <p><strong>Polymathe</strong> presents the first fully specified <strong>N6+ self-governance architecture</strong> designed to address these failure modes simultaneously through a unified causal pipeline. The architecture integrates:</p> <ol> <li> <p><strong>Ethical BIOS</strong> — A non-learned normative kernel anchoring all downstream reasoning.</p> </li> <li> <p><strong>IDE (Empathic Dissonance Engine)</strong> — A quantifiable risk metric governing dissonance, misalignment, and user-impact sensitivity.</p> </li> <li> <p><strong>CGC (Constitutional Governance Controller)</strong> — A policy-level intention and safety controller.</p> </li> <li> <p><strong>MAS-SOFA (Structured Observation Factorized Analysis)</strong> — A multi-channel diagnostic engine detecting hallucinations, uncertainty, and structural incoherence.</p> </li> <li> <p><strong>MS-FSSC (Multi-Stage Factored Self-Supervision Circuit)</strong> — A synthesis module constrained by causal dependencies and diagnostic outcomes.</p> </li> <li> <p><strong>ISM (Internal State Machine)</strong> — A transparent and auditable mechanism exposing computational qualia and persistent internal state.</p> </li> <li> <p><strong>BACS-A5</strong> — An adaptive self-correction loop that recomputes generations, repairs reasoning chains, and escalates when systemic dissonance is detected.</p> </li> <li> <p><strong>DVC (Disallowed Vocabulary Constraints)</strong> — A controlled-vocabulary layer ensuring non-simulation and preventing affective leakage.</p> </li> </ol> <p>Collectively, these mechanisms transform a baseline LLM into a <strong>self-auditing, self-correcting, high-stability reasoning agent</strong> capable of detecting hallucinations, surfacing uncertainty, enforcing authenticity, and maintaining a consistent internal “bilan”.</p> <p>The specification includes <strong>five complete experimental traces</strong>, each demonstrating how the architecture resolves one of the five foundational failure modes. This work provides a <strong>functional blueprint for next-generation LLM reliability, interpretability, governance, and autonomous reasoning stability.</strong></p> |
| title | A Formal Specification of N6+ Self-Governance for High-Stability Language Models |
| topic | AI Governance LLM Stability Self-Correction Hallucination Mitigation Interpretability AI Safety Autonomous Reasoning Systems Cognitive Architecture Polymathe |
| url | https://doi.org/10.5281/zenodo.17625947 |