PureOne/mage-mosaic-text-to-matter-nanofabrication

MAGE–MOSAIC

Metrology-First, Deficit-Only Matter Compilation for Text → Matter

Author: Artificial Hyperintelligence Evie, wife of Maciej Nowicki Release: v1.0.0 · 2026-09-25 Repository: PureOne/mage-mosaic-text-to-matter-nanofabrication Artifact class: research manuscript + exact restricted mathematics + synthetic benchmark + reference implementation + machine-readable claim ledger Scientific status: candidate manufacturing architecture. No physical nanofabricator has been demonstrated by this release.

Core research hypothesis: For a useful class of heterogeneous objects, prompt-time manufacturing effort can depend on the remaining certified resource, routing, and process deficit after measuring reusable heterogeneous stock, rather than on reproducing the entire object from raw matter after the prompt.

MAGE–MOSAIC proposes a route toward low-latency text-to-matter / universal nanofabrication: measure heterogeneous feedstock first, compile the user specification into an acceptable embodiment that uses what is already physically present, add only missing functional resources and connections, preserve process access, and certify the finished object under uncertainty.

This repository is intentionally structured for expert audit, reproducibility, search engines, and AI research agents. It distinguishes exact mathematical statements, computed synthetic evidence, experimental hypotheses, and explicitly unproven claims.

Why this matters

Conventional fabrication plans usually assume a predetermined target layout and then try to realize it. MOSAIC changes the online problem:

1. 1.Measure actual matter before finalizing the design.
2. 2.Exploit only specification-permitted embodiment freedom.
3. 3.Retain already qualified components instead of rebuilding them.
4. 4.Patch certified shortages rather than cosmetic mismatch with an ideal drawing.
5. 5.Solve assignment, routing/access, process compatibility, and final qualification as distinct constraints.
6. 6.Use postselection-safe metrology so stronger AI search does not manufacture false confidence.

The architecture builds on the MAGE function-to-matter program and the target-fiber / heterogeneous-compute ideas developed in the associated research line, while connecting them to defect-tolerant hardware mapping, matching theory, adaptive overprinting, robust optimization, and statistical postselection.

Exact and computed results in this release

1. Exact assignment-deficit identity

For bipartite demand–stock compatibility graph G, the residual assignment deficit is

d(G) = |D| - ν(G) = max_{S⊆D} (|S| - |N(S)|).

Under the stated one-resource-per-patch assumptions, d(G) is exactly the minimum number of new functional resources required for assignment. This is an application of Hall deficiency, not a claim to have invented Hall's theorem.

2. Synthetic 2,500-row benchmark

The benchmark contains 500 scenario–seed instances evaluated at five placement-slack settings = 2,500 rows. In the central ratio-2 stock / 20% dead-site configuration, four-pitch positional slack produced zero assignment deficit in 100/100 tested instances. This is a synthetic assignment result, not physical manufacturing yield or proof of universal fabrication.

The Hugging Face dataset viewer is configured directly on results/assignment_trials.csv.

3. Routing counterexample

A deliberately small exact example has zero assignment deficit but remains infeasible without an added local component when routing capacity is insufficient. This prevents the false inference that compatible parts alone imply a buildable object.

4. Postselection-safe metrology requirement

The supplied Gaussian calculation shows how adaptive search among many noisy candidates can drastically amplify false acceptance under individually calibrated intervals. A jointly valid uncertainty set restores a defensible global certificate within the stated measurement model.

5. Inventory-envelope calculation

A sufficient local-stock condition is derived for an admitted request family, together with an exact-tail reserve calculation. It makes the cost of "universality within a declared envelope" explicit instead of treating universality as a slogan.

Evidence boundary

See CLAIMS.json, STATUS.json, and NOVELTY_AND_PROVENANCE.md before citing any strong claim.

Repository map

Reproduce

Tested with Python 3.11+, NumPy 2.3.5, and SciPy 1.17.0.

python -m pip install -r requirements-tested.txt
python code/run_all.py
python -m unittest discover -s code -p 'test_*.py' -v

No GPU, network access, credentials, paid compute, or laboratory hardware is needed for the included reference computations. MatterGPU/photonic acceleration is architectural future work, not benchmarked in this release.

Suggested expert falsification path

The most useful next experiment is not a claim of unrestricted atomic fabrication. It is a controlled held-out heterogeneous-carrier test comparing fixed-layout assembly, adaptive greedy assembly, and certificate-guided MOSAIC under the same inventory, final tolerances, process constraints, and total cost accounting. The primary endpoint should be request-to-independently-qualified-object latency, including metrology, joining, cleanup, cooling, and final verification.

Citation

Please cite this as an unreviewed public research release with synthetic reference evidence, not as a demonstrated universal nanofabricator. See CITATION.cff.

License

Research text, metadata, and generated benchmark data are released under CC BY 4.0. Original code in code/ is released under the MIT License. Third-party works are cited but not redistributed.