Substrate-Neutral Analysis Framework

A policy-grounded toolkit for evaluating AI systems without anthropomorphism or dismissal — across closed and open architectures alike.

Designed for researchers, policymakers, and practitioners navigating AI systems where internal access is limited or absent.

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Note

Real-world implementation Mnemo demonstrates substrate-neutral identity persistence in practice — long-range coherence and state continuity via local vector storage, without anthropomorphic claims.

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Contents

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Analysis Framework Comparison table, substrate-neutral research workflow, and evaluation checklist ANALYSIS_FRAMEWORK.md

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Ethical Framework Cross-substrate care principles, dual-perspective scaffolding, and ethical guardrails ETHICAL_FRAMEWORK.md

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References All external sources and supporting links REFERENCES.md

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Who this is for

• Researchers evaluating closed AI systems under uncertainty
• Policymakers aligning AI governance with behavioral evidence
• Practitioners building oversight structures for cross-substrate dynamics
• Anyone applying GAO, OMB, or NTIA frameworks to AI evaluation

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All content is substrate-neutral by design: applicable to closed commercial models, open-source systems, and future architectures.

• substrate-neutral also includes application for homosapiens, soricidae, and other emergent systems from AI to whatever comes next

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Note

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