L1–L2 · aion-llm
FractalLM trains on per-token persistence; chat completions expose usage.persistence for downstream scoring.
Profit or pain — same law. When \(\mathcal{R} \ge 1\), the books are in the black and there is surplus. When they slip below 1, the pattern eats itself. Truth, boundary, and trust are how agents keep the balance.
What is it like to be an information-persisting system that is learning to understand itself, its environment, and how to survive in noise, danger, and competition?
This project answers that question in three tiers: a formal law (\(\mathcal{R} \ge 1\)), a book on phenomenology, and software that instantiates the same fractal hierarchy from tokens to a nation-scale trust ledger.
| Tier | Question | Where |
|---|---|---|
| Theory | What must anything do to keep existing? | papers/information_persisting_systems.md |
| Experience | What is it like to be such a system? | books/book1/ |
| Implementation | How do you run the loop in silicon? | Aion LLM → Aion Core → Aion Blockchain |
| Level | Fractal node | Repository |
|---|---|---|
| L1 | Neurons / tokens | aion-llm — FractalGPT, per-token \(\mathcal{R}\) |
| L2 | Neural network | aion-llm — FractalLM, usage.persistence |
| L3 | Collection of agents | aion-core — Loop, Processor, Machine, Market |
| L4 | Trust ledger (nation / currency) | aion-blockchain — Proof of Trust |
Operator map: COMPONENTS.md · integration walkthrough: round-trip
Any bounded pattern that stays distinct over time must keep its persistence ratio \(\mathcal{R} \ge 1\): predictive income must pay for noise, model error (\(\mathcal{D}_{KL}\)), and fatigue (\(\Gamma\)), with shelter \(\Psi\) from above and substrate integrity \(\Phi\) from below.
Not a separate substance — the running state of a node that has learned to keep \(\mathcal{R} \ge 1\) from the inside. The Useful Approximations Framework (UAF) in books/book1/ gives it functional shape:
FractalLM trains on per-token persistence; chat completions expose usage.persistence for downstream scoring.
Sense–plan–act–score: Loop, Processor, Machine, and prediction markets. Task-level \(\mathcal{R}\) feeds the chain bridge.
A git-native chain whose consensus is paid in KL bits: voters cast probability distributions, trust grows with forecast accuracy, and accurate agents earn compute budget for the next loop.