Name: ONTO Standard
Author: ONTO Standards Council

Question 1

What is ONTO?

Accepted Answer

ONTO is the standard for epistemic discipline in AI. The ONTO Standards Council is an independent institute maintaining the Central Law of Reflected Causality — an information-conservation law derived from five axioms. Three deployments of one standard: Regulator (grading with cryptographic proof), Agent (discipline layer for any AI), Human AI (protocol to retrofit or build a disciplined model from birth).

Question 2

What is the Central Law of Reflected Causality?

Accepted Answer

An information-conservation law: a response cannot carry more verified information than its cited sources contain. Derived from five axioms — Landauer (physicality), Kolmogorov (complexity), Eigen (self-organization limit), Shannon (channel capacity), and the ONTO contribution (sufficiency). Each axiom is independently established. Full formal derivation is published in the research paper.

Question 3

What is IGR?

Accepted Answer

Information Gap Ratio. A bounded [0,1] measure of the gap between a response's claims and the information content of its declared sources. Reproducible, falsifiable, measured per response — not per model. Four thresholds classify the gap: sufficient, partial, critical, bound-exceeded. Full formal definition in the research paper.

Question 4

Is ONTO a prompt or a framework?

Accepted Answer

Neither. ONTO is a derived law with measurable output. A prompt says 'be accurate.' A framework is a convention someone invented. The Central Law of Reflected Causality is derived from five axioms of information theory — the same way thermodynamics is derived from conservation principles. Reference implementations apply the law at inference time.

Question 5

How is ONTO measured?

Accepted Answer

Every response receives an IGR score and a 104-byte Ed25519 cryptographic proof: timestamp + content hash + digital signature. Scoring is deterministic — same input, same score, every time. Zero AI in the evaluation layer.

Question 6

Who needs the standard?

Accepted Answer

Anyone who uses AI for decisions that matter — health, finance, law, education, defense. For governments: the enforcement instrument for AI regulation. For AI providers: provable quality without retraining. For enterprise: compliance-ready AI with audit trail. For individuals: protection from fluent fabrication.

Question 7

Can I evaluate the standard right now?

Accepted Answer

Yes. The Agent deployment is live at ontostandard.org/agent. Enter any AI provider API key (OpenAI, Anthropic, Google, xAI, DeepSeek), ask a question, see baseline and disciplined answers side by side with IGR scores for both. Pilot evaluation available for organizations via council@ontostandard.org.

Question 8

Does ONTO modify the AI model?

Accepted Answer

No. The model is never modified. No retraining, no fine-tuning, no weight changes. The discipline is applied at inference time. Removing the standard returns the model to baseline immediately.

Question 9

Does ONTO slow responses?

Accepted Answer

No. Provider integration uses SSE(0) architecture — zero additional requests to the institute per AI response. The reference implementation is cached locally after initial delivery. The only added cost is context window size.

Question 10

What data does the institute collect?

Accepted Answer

Prompts and AI responses are not stored or retained. Only metadata is processed: token counts, IGR scores, timestamps, cryptographic hashes. Content passes through memory only and is discarded after scoring.

Question 11

Is the standard open or proprietary?

Accepted Answer

The methodology and scoring engine are public at github.com/nickarstrong/onto-research. The reference implementation is commercial. Complete derivation accompanies standard adoption.

Question 12

Is ONTO approved by EU AI Act?

Accepted Answer

The ONTO Standards Council is not a regulatory body. The standard is an instrument that produces quantitative evidence for EU AI Act compliance — specifically Articles 9, 13, and 15. Every evaluation is cryptographically signed, creating an auditable proof chain.

Question 13

Is ONTO related to any blockchain or token?

Accepted Answer

No. The ONTO Standards Council works exclusively on AI epistemic discipline. No blockchain, no cryptocurrency, no tokens. Name similarity is coincidental.

Question 14

Who maintains the standard?

Accepted Answer

ONTO Standards Council — an independent institute founded in 2024. Not a corporation, not a government agency. The standard is designed to remain independent from any single AI provider, government, or investor. Contact: council@ontostandard.org.

Question 15

How is ONTO different from AI safety work like RLHF?

Accepted Answer

RLHF restricts what a model can say. Epistemic discipline, by construction, specifies how a model must say it when it speaks — with source, with calibrated confidence, with scope, with falsifiability. These are complementary layers addressing different problems.

ONTO — The standard for epistemic discipline in AI

ONTO Standards Council

AI makes claims.
ONTO makes them measurable.

Medicine

Law

Finance

Government

Education

Defense

I regulate AI

I build AI

I deploy AI

I evaluate AI infrastructure