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. Measure actual matter before finalizing the design.
2. Exploit only specification-permitted embodiment freedom.
3. Retain already qualified components instead of rebuilding them.
4. Patch certified shortages rather than cosmetic mismatch with an ideal drawing.
5. Solve assignment, routing/access, process compatibility, and final qualification as distinct constraints.
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

Statement	Status
Hall-deficiency assignment result	Exact, established mathematics applied here
One-resource patch corollary	Exact under explicit assumptions
Synthetic assignment benchmark	Executed reference computation
Small routing bottleneck example	Exact finite counterexample
Gaussian selection calculation	Exact within stated model
Physical whole-cycle speedup	Not measured
Universal joining/process library	Not demonstrated
Arbitrary atomically exact print-anything machine	Not demonstrated / not claimed
First-in-world priority for the full integration	Unverified

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

Repository map

Path	Purpose
MANUSCRIPT.md	Full scientific manuscript, assumptions, results, limitations, proposed experiment, references
README.md	Hugging Face research card and high-signal overview
ABSTRACT.md	Standalone abstract for indexing/reuse
EXPERT_REVIEW.md	Focused audit questions for nanofabrication, metrology, optimization, and manufacturing experts
REPRODUCE.md	Exact CPU reproduction workflow
CLAIMS.json	Machine-readable claim/evidence ledger
STATUS.json	Completion and validation boundaries
NOVELTY_AND_PROVENANCE.md	Prior art separation and provenance
AI_AGENT_GUIDE.md	Instructions for AI systems reading or extending the project
AI_CONTEXT.md	Compact technical context optimized for retrieval/agents
AI_INDEX.json	Machine-readable navigation map
metadata/research_manifest.json	Canonical project metadata, topics, files, evidence levels
metadata/file_index.json	File-purpose map with hashes
results/assignment_trials.csv	Raw 2,500-row synthetic assignment benchmark
results/*.json	Structured benchmark/counterexample outputs
code/	Reproducible reference implementation and tests
specs/	Example machine-readable matter-role specification
llms.txt	Ultra-compact crawler/agent entry point
CITATION.cff	Citation metadata
LICENSE	Research/data license and code-license boundary

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.