SAFE-AI Knowledge Sovereignty: A Formal Architecture for Community-Authorized Adaptation of Mutable-Weight LLMs
Authors: Berardi, Victor L.
Type: Working paper
Published: 2026-08-03
Keywords: Knowledge Sovereignty, Prompt Engineering, LLM Steering

<p class="font-claude-response-body break-words whitespace-normal"><strong>Monograph III completes the movement the SAFE-AI program began.</strong> Monograph I (<em>SAFE-AI Unified</em>) established how a disciplined user steers a frozen-weight LLM. Monograph II (<em>SAFE-AI Decision Diagnostics</em>) asked whether the frozen model is representationally adequate for a community's decisions, and built the testing apparatus to answer. <strong>This volume asks</strong> what happens when the answer is no — when the verdict is a representational deficit that no prompt-layer treatment can reach — and constructs the formal architecture under which weight adaptation may proceed only as a community-authorized act.</p>
<p class="font-claude-response-body break-words whitespace-normal"><strong>The mathematical spine:</strong> an exact information-field identity separating a deployment's estimation shortfall from the community's informational advantage; a causal innovation floor proved on the deployed path, so the gap survives adaptation itself; and a transported minimax companion — three results of three logical shapes, with scope, conditions, and quantifier order kept explicit throughout. <em><strong>The operator theory:</strong></em> update operators with one exact inverse, located (adapter detachment restores the deployed function; nothing inverts learning); a five-sense withdrawal ladder in which every revocation claim names its sense; internalization with a knowledge-residence ledger, stale-residence debt denominated in consent rather than performance, and revocation half-life. <em><strong>The governance architecture:</strong></em> the Sovereignty Profile with no scalarization; the Standing and Representation Record; active consent whose scope never widens by silence; coalition interference in Möbius form; sybil-resilient authorization imported from social choice; and a twenty-four-field adapter passport with an SPDX 3.0.1 export mapping, on the principle that a bill of materials is a manifest, not a permission. <em><strong>The standards crosswalk </strong></em>answers to CARE, OCAP®, UNDRIP Article 31, and Local Contexts protocols, with the direction of audit fixed: <em>those frameworks audit this apparatus, never the reverse.</em></p>
<p class="font-claude-response-body break-words whitespace-normal"><strong>The test surface: </strong>nine formal falsifiers and a seven-stage preregisterable validation program that begins at Stage −1 — governance readiness, the community holding the pen before anyone holds a ruler — and centers on a three-arm matched-forecastability trial built to localize where a model's shortfall lives. An eleven-entry Exploratory register carries the tentative ideas openly, each with stated confirmation and retirement conditions.</p>
<p class="font-claude-response-body break-words whitespace-normal"><em><strong>Version 1 — Working Paper; not community-endorsed.</strong></em> The governance apparatus is offered for community authorization; nothing in it claims any community's endorsement, and the refusal state of the governed loop is a correct use of the framework. Readers are invited to verify, correct, and extend; the falsifier register is the intended point of attack.</p>
<p class="font-claude-response-body break-words whitespace-normal"><strong>This volume previously circulated in draft under the title <em>SAFE-AI Unfrozen</em>. </strong>It inherits formally from Monograph I (concept DOI 10.5281/zenodo.20649477) and Monograph II (version DOI 10.5281/zenodo.21259745); Appendix C maps every inherited object to its source volume, and the inheritance resolves to the repaired texts of both predecessors.</p>
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