Sheldon K. salmon

AboutI'm Sheldon K. Salmon — an AI Systems Architect who builds the frameworks that validate, harden, and prevent the failure of AI before it costs you everything.Most people build AI that works. I build AI that knows when it doesn't work — and has the structural honesty to say so before the damage is done.My work lives at the intersection of adversarial validation, epistemic rigor, and structural integrity. I designed the AION, CRP, GENESIS, FSVE, and BAPL frameworks — a suite of interlocking systems that treat every AI-generated assertion as potentially catastrophic until independently verified. Not out of paranoia, but out of precision.I don't do black-box outputs. I don't accept performance as a substitute for legitimacy. If your system can't decompose its own reasoning, map its own failure modes, and prove its own claims — it isn't ready. My job is to make it ready, or to tell you honestly that it isn't.Explore the core architecture and ongoing development in the open-source AION-BRAIN repository: https://github.com/AionSystem/AION-BRAIN.

Core Frameworks
(Aion-AI-Auditor Stack)
https://aionsystems.carrd.coA sovereign, self-constrained auditing interface built on four interlocking layers — all normalized [0,1], self-applied, with M-MODERATE convergence:FSVE v3.0 — Foundational Scoring & Validation EngineSix non-interchangeable score classes: Confidence (intent structure), Certainty (challenge resistance), Validity (meta-legitimacy), Completeness (surface coverage), Consistency (internal coherence). Enforces five non-negotiable principles: No Free Certainty, Uncertainty Conserved, Scores Are Claims, Invalidatability Required, Structural Honesty Precedes Accuracy. Hard threshold: Validity < 0.40 → all downstream suspended.AION v3.0 — Structural Continuum ArchitectureMeta-analytical evaluation: system identity mapping, failure-state extraction, signal propagation modeling. Delivers compound SRI fragility, multi-perspective review (5 reviewer types), required concrete outputs (artifact + node + behavior-kill), and ecosystem-level constraint mapping.ASL v2.0 — Active Safeguard LayerExecution-time governance: dual-watchdog architecture, multi-modal interlocks, Bayesian adaptive thresholds, graduated response tiers (5 levels), operator attention budget, framework independence fallback. Designed for graceful degradation and runtime enforcement of upstream findings.GENESIS v1.0 — Generative Engine for Novel PatternsDiscovers, validates (7 legitimacy axes + PLS score), translates causally (not metaphorically), and composes algorithms with integrity guarantees (CIS score). Enforces pattern lifecycle, decay modeling, and library governance.Shared DisciplineUnified Validation Kernel (UVK), Operational Definition Registry (ODR), Nullification Boundary Protocol (NBP), Framework Calibration Log (FCL), multi-perspective red-teaming, refusal to silently erase uncertainty.Intended UseForensic analysis of AI incidents • Zero-trust scoring of claims/models • Systemic fragility & cascade mapping • Extraction of reusable failure/mitigation patterns • Composition of hardened safeguards.Strict DisclaimerRESEARCH & RED-TEAM PROTOTYPE ONLY. Theoretical architecture. No live validation, no FCL entries, M-MODERATE convergence. Confidence ceiling remains low until empirical grounding. NOT for production deployment, medical/legal/regulatory decisions, or high-stakes use. All outputs require independent verification by domain experts.---Personal Quote"A system that cannot explain how it fails is not a system — it is a liability waiting for the right conditions."— Sheldon K. Salmon---If your team deploys AI in regulated domains (healthcare, finance, autonomy, crisis response) and needs architectural proofs instead of just benchmarks — reach out. I consult on forensic audits, fragility mapping, and hardening generative systems where failure isn't an option.