Autopoietic Project Generation

Daniel Ari Friedman

doi:10.5281/zenodo.21227869

Overview

template_autopoiesis is a combinatoric grammar that deterministically generates
whole runnable projects — not files or snippets, but complete, independently
testable child repositories with their own kernel source, tests, analysis
entry point, and manuscript. A single integer seed plus a grammar of orthogonal
slots (primitive domain, analytical track, section set, and three
presentation/provenance slots) selects one child from a combinatoric product
space of 360 nominal (45 content-distinct)
configurations, via a SHA-256 digest of the seed and slot identity — with no
random-number generator anywhere in the expansion path.

Project generators routinely claim completeness, determinism, and
traceability without making any of the three independently checkable. This
exemplar treats each claim as a structural property to verify rather than a
rhetorical one to assert: verify_child() recomputes a tree hash from the
files actually on disk and compares it against the recorded provenance,
rather than trusting a value the same run wrote down; a honesty manifest
inspects the live source AST to confirm every claim in this manuscript
resolves to a real function in a real file; and a per-domain mutation gate
checks that the acceptance tests reject a constant-success stub before
trusting that they accept the real kernel. The same discipline governs this
document itself — every number below is substituted at render time from a
live measurement rather than hand-typed as a literal.

Across 5 heterogeneous primitive domains
(- optimization
- dynamics
- statistics
- signal
- graph), 493 tests exercise both fixed ground-truth
checks and Hypothesis-driven property invariants at 96.28% branch
coverage, with an explicit negative control per domain distinguishing the
real kernel from a deliberately-wrong one.

Generation pipeline

Grammar product space

- Domain count: 5
- Effective product size: 45
- Total product size: 360
- Reserved slots: 3 (figure_profile, qr_profile, integrity_profile)
- Grammar hash: f84a8f9dbcb18e37
- Tests: 493 · Coverage: 96.28%

---
Associated artifacts
GitHub release: Autopoietic Project Generation (v1.0.1) — improved abstract (https://github.com/docxology/template/releases/tag/v1.0.1)
DOI: https://doi.org/10.5281/zenodo.21227869
Zenodo: https://zenodo.org/records/21227869
PDF SHA-256: 84145371f10c7ee97a75c53d78917d247fd6bd987d2c1764ee437b6fbdef8f51

autopoiesiscombinatoric grammardeterministic generationproject synthesisreproducible researchinfrastructure automation

Overview source: Curated paper metadata.

Methods and contributions

Read the source for the full argument, qualifications, and evidence.

Findings and contributions

The grammar's nominal product space is 360 cells but only 45 are effective once the 3 presentation/sealing reserved slots are excluded; both are reported.
The paper aims to make completeness, determinism and traceability of generated projects structurally verifiable by re-running code rather than asserting them in prose.
On the example input, iterative gradient descent and the closed-form minimiser agree to within 1e-4.
The reported build has 493 tests at 96.28% coverage, with values injected from a live measurement at render time.
A stated limitation: reserved slots do not yet affect materialization, so distinct seeds can yield different spec hashes but byte-identical children.

Methods

Combinatoric slot grammar in config.yaml with SHA-256 grammar hash — A grammar of orthogonal slots and options is parsed and validated by parse_grammar(); its canonical JSON is hashed to fingerprint the grammar.
Five-stage pure-function spine: load, expand, materialize, verify, seal — Child projects are generated by five pure-function stages in separate modules, with no interactive state or network access.
Entropy-free seeded slot selection via SHA-256 digest modulo option count — Each slot choice is derived from a digest of seed, slot name, ordinal and options, with no RNG calls in the expansion path.
Tree-hash provenance re-computed from disk by verify_child() — materialize() records a tree hash of sorted (path, content_hash) pairs; verify_child() re-reads files and recomputes it rather than trusting the record.
Primitive kernels with analytic checks and a mutation meta-gate — Kernels are checked against analytic outputs; a mutation meta-gate (test_meta_teeth.py) asserts per domain that a constant-success stub fails and the real implementation passes.

Summary sources: Paper metadata and evidence · Extracted source text.