CiteSig

The problem

The internet has no machine-readable way to distinguish a verified fact from a plausible-sounding sentence. HTML has no <verified> tag. Search engines guess. Language models hallucinate. Regulators are starting to require provenance for AI outputs, and no standard yet exists to provide it.

CiteSig is the missing layer: a small, open protocol that lets publishers, journalists, researchers, and AI systems attach a signed reference to any factual claim — and lets browsers, verifiers, and downstream tools check that reference at rest and at render time.

Example — inline claim

<!-- A claim, without CiteSig -->
<p>The Great Wall of China is visible from the Moon.</p>

<!-- A claim, with CiteSig -->
<p data-citesig="cs:v1:9f2a...c8e1"
   data-verdict="refuted"
   data-confidence="0.02">
  The Great Wall of China is visible from the Moon.
</p>

The data-citesig attribute references a canonical claim record. Any verifier — a browser extension, an LLM output filter, an editorial workflow — resolves it against a public registry and returns a signed verdict.

Design principles

• Neutral standard, not a product. The spec, the reference implementations, and the registry contract are open. Anyone can run a verifier node.
• Cryptographically grounded. Ed25519 signatures. Content-addressed claim IDs. Independently verifiable without trusting a central authority.
• Human-readable, machine-checkable. Works in HTML, JSON, Markdown, plain text, and streaming LLM outputs. Degrades gracefully when unsupported.
• Composable with existing standards. Complements C2PA (media provenance), schema.org/ClaimReview, and W3C DIDs. Fills the missing text-claim slot.

Why a standard, not a product

Most AI-trust tools score whether an answer seems correct — a probability, delivered inside a dashboard. CiteSig works at a different layer. It produces a cryptographic receipt that travels with the claim itself and lets anyone verify where the AI got it.

• A protocol, not a platform. Adoption is the moat, not lock-in. Like OAuth or DKIM, the value grows as more publishers and models emit signed claims.
• Cryptographic, not statistical. Verifiers return a signed verdict, not a confidence score. Proof survives the model call and can be checked years later.
• Provider-agnostic. Works with any model, any host, any surface — HTML, JSON, Markdown, streaming LLM output. Not tied to a single vendor's stack.
• Complements, does not replace. Existing quality tools can consume CiteSig verdicts as an input signal. Governance and audit teams get the provenance trail they already need.

Status

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Thanks — you'll hear from us when v0.1 lands.