Project summary

CIOSA formalizes certification of semantic understanding as a game between

an adversary and a behavioral certifier, and proves the game value is exactly

1/2 for every finite evidence size: no amount of behavioral evaluation can

certify that a system understands. The alignment component is bounded below

by Rice's theorem — scaling cannot move it. I have already verified this on

small transformers (full reproducible pipeline: scripts, data, charts, MIT

licensed, under journal review). This project scales the verification to

real models (7B-70B) to empirically confirm the bound at production scale,

and publishes the numbers openly. The societal point: if scaling cannot

purchase certified understanding, then compute spent purely on

scaling-for-trust is wasted — the bound justifies redirecting resources

to architectures that can actually be audited.

What are this project's goals? How will you achieve them?

1) Run the certification game on 7B-70B models and confirm the value stays

at 1/2 empirically. 2) Publish all data, scripts, and charts openly.

3) If funding allows, stand up a small research lab in Egypt dedicated to

verifiable computing, continuing the work full-time.

Method: extend the existing pipeline (twin-model construction, game value

measurement, identification curves) to larger open models; the repo already

reproduces every number deterministically, so scaling only adds size, not

new machinery.

How will this funding be used?

Roughly: compute for the 7B-70B certification experiments (GPU cloud), a

living stipend for a full-time gap year so I stop freelancing, and — at the

upper end — equipment to seed a small lab in Egypt. A detailed budget lives

in the repo.

Who is on your team? What's your track record on similar projects?

Sole researcher. Three IEEE papers accepted within four months (CNS CPSSec,

AIITA, ICDPN), a separate empirical result on LLM hallucination ablation

(detection AUROC 0.968; causal ablation p=0.017), and a habit of publishing

negative results publicly — the NULL ablation in No-Hallucinations-Ever is

in the first commit. Full-time, no lab politics.

What are the most likely causes and outcomes if this project fails?

Honestly: the bound is already proven theoretically, so the largest risk is

that the scaled experiments confirm it without surprise — meaning the

empirical contribution is confirmation, not novelty. Second risk: compute

is insufficient for the largest models and the scale-out stalls. Third: the

field may simply not care about certification impossibility. Mitigation:

the first two are testable within weeks on small models; the third is

inherent to research value judgment.

How much money have you raised in the last 12 months, and from where?

No grants received. Applications pending at Emergent Ventures, TAIF, and

an SFF round; this project is independent of them.