Project summary:

AI can generate convincing interpretations of ancient inscriptions and artifacts in seconds, but there is no clear public way to verify whether those claims are grounded in evidence. Even careful readers can mistake persuasion for truth. This project builds a simple, public system to test those claims.

Egyptian hieroglyphics are the starting point because the script is already understood. That makes Egyptian the right control case: AI can be tested against known answers instead of guesses. If the method works here, it can later be extended to more uncertain scripts, including Meroitic, and eventually to harder undeciphered-script claims.

Example: one Egyptian claim tested through evidence, reviewer checks, AI comments, traffic-light labels, and one recorded result.

What I built

I created a one-page prototype called the Falsifiability Sheet: a structured record for testing one interpretation claim. Each sheet captures the claim, evidence, reviewer notes, AI comments, and a final traffic-light outcome: Green = supported, Yellow = uncertain, Red = unsupported.

Why Egypt first

This pilot starts with Egyptian hieroglyphics because the script is already understood. That makes Egyptian the right control case: the method can be tested against known answers instead of guesses. I am not trying to re-decipher Egyptian; I am using it as a stable testing ground to evaluate claims clearly, repeatably, and publicly.

What this pilot will produce

The pilot will produce 10 completed Egyptian test sheets, reviewer checks where possible, AI traffic-light labels, a public dataset, and a short report showing which claims held, weakened, remained unresolved, or failed.

Do not just trust an interpretation. Test it.

How this project works: This project works at the claim level: one claim, one evidence trail, reviewer checks where possible, one AI record, and one public result.

Goals:

• Test whether the Falsifiability Sheet can evaluate interpretation claims using Egyptian hieroglyphics as the control case

• Produce 10 completed Egyptian test sheets

• Publish a public dataset showing which claims held, weakened, remained unresolved, or failed

• Show whether the method can scale from a known control case toward more uncertain ancient-script claims

How: I will run a 10-case pilot using Egyptian hieroglyphic claims with already-known answers. A case means one claim tested against one object, image, or inscription.

1. Build the case record: Each case gets one Falsifiability Sheet containing the claim, evidence, uncertainty labels, reviewer notes, and final outcome.

2. Test the claim against evidence: AI will not guess freely. It will review only a fixed evidence packet: the image, claim, evidence, notes, and uncertainty labels.

3. Record the result publicly

Each case receives one traffic-light result:

Green = supported

Yellow = uncertain or unresolved

Red = unsupported, overconfident, or failed

Where possible, R1/R2 reviewers — two independent readers — will check the evidence trail. The final output will be a public dataset and short report showing which claims held, weakened, remained unresolved, or failed.

Why this can scale:

The method is not tied to one script. It is tied to a repeatable process: one claim, one evidence trail, restrained AI review, reviewer checks where possible, and one recorded outcome.

Egyptian hieroglyphics provide the known baseline. Meroitic or other partially understood scripts would provide the next test of uncertainty. Undeciphered materials would come later, only after the method has been tested on easier ground first.

The broader aim is not to solve every ancient script. It is to build a reusable way to test interpretation claims as uncertainty increases.

How funding will be used:

$7,000 — Project lead (analysis, case preparation, documentation)

$3,000–$4,000 — Independent reviewers (R1/R2)

$1,000–$2,000 — Publishing, dataset preparation, and materials

Minimum ($12,000): complete a 10-case pilot with full dataset and report.

Goal ($15,000): expand to 12–15 cases, increase reviewer coverage, and strengthen dataset quality across a four-month timeline.

Who is leading this project? This project is led by Michael Grasa, an independent researcher working at the intersection of ancient-script interpretation, AI verification, and public falsifiability tools.

This work builds on prior support from Emergent Ventures (Mercatus Center, George Mason University) and conference-stage research presented in New Delhi, India, in 2025, with proceedings forthcoming. That conference-stage work began as Version 1 of the method and has since developed into the current Version 5 Falsifiability Sheet. The conference abstract appears on page 43 of the Book of Abstracts.

The framework has been developed through multiple iterations (V1–V5) and includes structured peer review (R1/R2) and AI accountability tracking.

Previous work and public outputs:

Emergent Ventures reference: Marginal Revolution search result: https://marginalrevolution.com/?s=Michael+Grasa

Conference abstract: Book of Abstracts, page 43: https://culture.gov.in/files/reports_documents/Book_Abstracts.pdf

Zenodo: DOI-linked research archive: https://zenodo.org/records/18518231

LinkedIn: public-facing updates and engagement: https://www.linkedin.com/in/michaelgrasa

GitHub: open project materials: https://github.com/mlge9900-crypto/echoes-of-the-script-openlab

The project is built around a simple public method: one claim, one evidence trail, reviewer checks where possible, AI comments, and one recorded traffic-light result.

Nonprofit and fiscal support is also available through Echoes of the Script for partners or donors who prefer a formal route.

Risks and outcomes if unsuccessful

Risks:

• Independent reviewers may disagree

• Some cases may remain unresolved

• The method may need revision before wider use

Even if the pilot is unsuccessful, it will still produce a public record of what worked, what failed, and where the method needs revision. The goal is not to force success, but to produce transparent, testable outcomes.

Prior funding

Emergent Ventures (Mercatus Center, George Mason University): approximately $12,000 received in the last 6 months.