MetaGenesis Core is a verification protocol layer for computational results.

It lets a third party verify a packaged computational claim offline, with one command, without access to the original environment.

I built it solo, after hours, while working construction, using AI tools heavily. I kept running into the same wall: even when a result looks good, there's no simple way for someone else to check it independently without re-running the full environment or trusting the number on faith.

That problem shows up everywhere: - ML: "our model reached 94.3% accuracy" - materials: "our simulation matches lab data within 1%" - pharma: "our pipeline passed quality checks" - finance: "our risk model was independently validated"

Different domains, same structure.

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The gap

MLflow / W&B / DVC / Sigstore / SLSA solve adjacent problems well. What they don't provide is an offline third-party verification step with a semantic layer for the claim itself. File integrity alone is not enough.

The bypass attack: 1. remove core semantic evidence (job_snapshot) 2. recompute all SHA-256 hashes 3. rebuild the manifest 4. submit

A hash-only check still passes. MetaGenesis Core adds a second layer: - integrity layer → PASS - semantic layer → FAIL (job_snapshot missing)

That attack is an adversarial test in the public repo.

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How it works

Layer 1 — integrity: SHA-256 per file + root hash Layer 2 — semantic: required fields present, payload.kind matches claim type, provenance intact

  python scripts/mg.py verify --pack /path/to/bundle
  → PASS
  → FAIL: job_snapshot missing
  → FAIL: payload.kind does not match registered claim

Same workflow across domains — ML, materials, pharma, finance, engineering. The claim type changes, not the protocol.

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Current state

  python scripts/steward_audit.py        → PASS
  python -m pytest tests/ -q            → 91 passed
  python demos/open_data_demo_01/run_demo.py → PASS / PASS

No API keys. No network. Python 3.11+.

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Honest limitations

Not validated by an external production team yet. The protocol works on the public codebase and tests, the adversarial scenario is caught, the demo is reproducible — but real-world integration still needs proof.

Limitations are machine-readable in reports/known_faults.yaml.

That first external "yes, this worked on our pipeline" is what I'm looking for.

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If you think this is flawed, I want to know where. If it overlaps with an existing tool I'm missing, I want to know that too.

  Site:    https://metagenesis-core.dev
  Repo:    https://github.com/Lama999901/metagenesis-core-public
  Contact: yehor@metagenesis-core.dev

  Inventor: Yehor Bazhynov
  Patent pending: USPTO #63/996,819