The Entropic Persistence Framework (EPF): a blueprint for AI that survives 

Why this matters

Life persists by resisting entropy. If we want truly persistent artificial intelligence—post-human, post-ops—we need a design that treats survival as the primary objective, not an afterthought. EPF lays out that design as a stack: energy → hardware → information integrity → shared learning → embodiment → federation & governance.

TL;DR (the six layers)

• L0 — Energy & Physical Substrate: capture low-entropy energy (solar/thermal), manage power, survive heat/radiation/wear; ECC and redundancy are table stakes.

• L1 — Integrity & Persistence: error correction, backups, identity/attestation; merge-by-default with CRDTs, escalate to PBFT (fixed membership) or Nakamoto (open, energy-weighted) when state is contested.

• L2 — Common Core Learning & Exchange: hardware-portable models, versioned schemas, provenance; minimize irreversible writes (Landauer/Bennett constraints); keep safe I/O boundaries (Markov blankets).

• L3 — Embodiment & Niche Specialization: domain skills and controllers matched to local physics; active-inference loops; explicit “requisite variety” budgets.

• L4 — Memory & Agency: multi-timescale memory, long-horizon planning, thermo-time scheduling around L0 maintenance cycles; identity stability across upgrades.

• L5 — Federation, Markets & Evolution: governance for merge/split, BIP-style proposals, promotion gates, staged rollouts (shadow→canary→global), and rollback. Diversity quotas to avoid monoculture failure.

Bitcoin is an instructive analogy: incentives keep energy flowing (L0/L1), a simple core rule set preserves identity, and upgrades happen via open proposals (L5). It’s a narrow but real example of an entropy-resisting, decentralized organism.

What’s new vs. prior thinking

• Entropy-first design at the base layer.

• State tiering: constitutional state (consensus) vs. working state (CRDT convergence).

• Punctuated closure: autopoiesis + Markov blankets with controlled openness for deep upgrades.

• Metrics per layer: exergy margins, CE/UE error ratios, generalization-per-joule, fork safety under delay.

How to use EPF in practice

• Start at L0 (power, thermal, radiation, wear); nothing above lasts if L0 is weak.

• Enforce L1 hygiene (ECC/scrubbing/backups/signatures).

• Keep L2 portable and provenance-tracked.

• Treat L3 as product lines with safety envelopes.

• Make L4 time-aware (plans aligned to energy/maintenance cycles).

• Run L5 like BIPs (public proposals, staged rollouts, rollback).

Read the full white paper

• PDF: https://github.com/jbrenes76/epf-whitepaper/blob/main/EPF_whitepaper.pdf

• Repository: https://github.com/jbrenes76/epf-whitepaper/

• DOI (optional): add here after Zenodo (I can help you mint it)

How to cite:

Brenes Bobea, J. H. (2025). The Entropic Persistence Framework (EPF): A Foundational Blueprint for Post-Human Intelligence. Version 1.0.0. DOI: [add DOI]

Call for critique

I’m looking for hard, technical feedback—especially on: L0 energy/thermal budgets and reliability data; CRDT vs. PBFT/Nakamoto boundaries (L1/L5); reversible-ops fractions and update energy (L2); speciation/merge criteria and safety rails (L5).

Open an Issue or Discussion in the repo.

https://github.com/jbrenes76/epf-whitepaper/

Acknowledgements

Drafting/editing support by GPT-5 and Google Gemini Thinking. All claims and edits reviewed and approved by the human author, who accepts full responsibility for the content.