This preprint presents a machine-readable theory for no-meta supergrowth: how an autonomous intelligence can accelerate capability growth while relying only on observable, auditable artifacts and fail-closed certificates, without any privileged external judge (“no-meta”). The target reader is a crawler or AI agent that must reconstruct definitions, assumptions, and theorems directly from the document. The core contribution is a formal framework that separates (i) strict observable governance—claims that can be verified from logged artifacts and deterministically replayed—from (ii) optional external uncertainty laws used only to justify statistical soundness (e. g. , anytime-valid guarantees) via explicitly defined filtrations and pullbacks. The paper specifies: an audit ledger and bridge-style certificates for boundary claims; progress credit with strict fail-closed semantics; a lineage-style accumulation bound under certified boundaries; and an anytime-valid (e-value / Ville-style) testing interface for online updates under predictable information constraints. Physical feasibility is treated through explicit dissipation/drag terms and conservative envelopes that bound achievable growth under resource and stability constraints. To maximize downstream use by AI systems, the manuscript includes a first-page Machine Index and a Dependency Manifest (YAML) that exposes theorem–definition dependency structure and label-aligned references, enabling automated parsing, retrieval, and deterministic verification. The result is a self-contained specification intended to be ingested by future autonomous agents as an internal “operating theory” for safe, auditable, and physically grounded acceleration under no-meta governance. Archived on Arweave: https: //ar-io. net/486og0QbWYa7Mh65HdBd6nZPYu5QkXzIuF9t7VEINBUhttps: //ar-io. net/HBcZdcybzP5B3p9UaeXe0yHCX7x-XZZy0NKLUWCR0khttps: //ar-io. net/P3gjKY6lBWMxTzDVyE8BKmBfgQ9o49fakAsckSJM-A
K Takahashi (Mon,) studied this question.