An MX Compatible PDF is an ordinary PDF that also carries three things most PDFs do not: a tagged structure tree, an AI-governance provenance record, and the metadata that ties both to a published canon. The badge on the cover is the visible index entry for that evidence chain. You can verify every part of it from the file alone.
This page tells you what is in the file you have, why it matters under the European Accessibility Act and the emerging AI-governance regimes, and how to verify any of the claims yourself. The QR code you scanned ends with a fragment of the form #mx:<hash>. That hash is the first eight characters of the source-content SHA recorded inside the document. It identifies which version of the source the PDF was rendered from. Anyone can match it back to the canonical source on disk or in the repository.
What this PDF carries
Three things, all machine-readable, all open by design.
A tagged structure tree
Inside the PDF, every heading, paragraph, list item, table cell, figure, and caption is recorded as a node in a logical tree. The tree follows ISO 14289-1, the international standard for tagged PDF, declared at PDF/UA Level 2 in the document's XMP packet under pdfuaid:Part=1. A screen reader can walk that tree the same way a sighted reader walks the visible page: heading by heading, paragraph by paragraph, alt text on every figure. An AI agent reads exactly the same tree. The structure is the file; it is not reconstructed from pixels.
For comparison, an ordinary PDF carries only the visual rendering. The order of text in the file is the order the typesetter wrote it, not the order a reader would follow. A screen reader has to guess. An AI agent has to do vision-based reconstruction. Both are slower, less accurate, and more expensive than reading a tree that is already there.
AI-governance provenance
Adjacent to the PDF on disk, and embedded as a single XMP payload inside the PDF itself, is a pair of provenance records. The AI sidecar (file extension .provenance.ai.json) records every non-deterministic step that touched the document: large-language-model calls, multi-agent collectors, human-committed actions. It serves the AI-governance regimes that ask "what model produced this, on what input, under what prompt, with what audit trail" — the EU AI Act, the UK ICO AI guidance, the United States NIST AI Risk Management Framework, the Colorado AI Act, and the analogous instruments now appearing in other jurisdictions.
The deterministic sidecar (.provenance.deterministic.json) lives next to it and records the rule-driven steps: gate verdicts, CSV checks, render steps, probe results. It serves the European Accessibility Act, Directive 2019/882, which entered into force on 28 June 2025 and asks "is this document conformant to the relevant accessibility standard, and what evidence supports that conformance".
Both sidecars cross-reference each other through a companion field. Together they form an evidence vehicle, not a compliance grant. Compliance with any law stays a legal duty of the organisation that publishes the file. What the records do is make the evidence the organisation must produce structured, machine-readable, tamper-evident, and verifiable on request.
The MX metadata packet
The document's XMP metadata carries an MX namespace with twenty-plus fields that record what the file is, who wrote it, when, against what canonical source, with what intended audience, under what status. The fields draw from MX, an open, community-governed standard administered by The Gathering. The wire schema travels with the file: a tool that opens the PDF without ever visiting a network can still read provenance, intent, audience, and canonical pointers directly from the metadata. The structure survives mirroring, archiving, and re-distribution.
Why it matters
Two pillars carry the value proposition. The first is that MX makes the content machine-readable: any system reading the file gets a complete, structured account of what it contains and how to use it. The second is that REGINALD, the registry for which CogNovaMX is the reference operator, makes the content machine-trustworthy: a signed attestation binds the file to a known publisher and a specific source hash, so a verifier can check that the document has not been altered since it was published.
The combined effect is that AI agents working from MX Compatible content cite attested facts rather than inferences. Hallucination rates drop. Inference cost drops by orders of magnitude, because the agent walks a tree it does not have to reconstruct. And the documentation that regulators require, under the EAA for accessibility, under the EU AI Act for high-risk AI systems, under the NIST AI RMF for risk management, becomes a structured by-product of how the file was built rather than a separate compliance artefact that has to be assembled by hand.
For a publisher, the upside is that the same metadata serves three audiences against the same fabric. Auditors walk from a regulatory clause to every decision that cited it via the policy-reference fields. Operational teams get a tamper-evident log of every action the file went through, sequence-numbered and signature-bound. Regulators verify each link with their own standard libraries: DID resolution, JCS, Ed25519, RFC 6962.
How to verify the claims
Everything in the file is open to inspection. Three commands cover most of the verification surface.
Check the structure tree
Confirm the PDF carries ISO 14289-1 tags:
qpdf --json file.pdf | jq '.objects[] | select(.["/Type"] == "/StructTreeRoot")'If the result is non-empty, the document has a structure tree. Walk further into the tree to confirm tags map to logical content. The structure-tree presence and the /MarkInfo /Marked true declaration are the Level 1 conformance signal under the MX Document Accessibility note.
Read the conformance claim
Confirm the document declares PDF/UA Level 2:
exiftool -XMP-pdfuaid:Part file.pdfA value of 1 indicates ISO 14289-1 Part 1 conformance is asserted. The declaration is open to verification by any tool that walks the XMP packet.
Read the provenance chain
The AI provenance payload is embedded in the XMP packet under xmp:ProvenanceAiPayload:
exiftool -b -XMP-mx:ProvenanceAiPayload file.pdf | jq .The -b flag is mandatory; without it exiftool prepends a label that breaks JSON parsing. The output is the full AI provenance chain: every step the document went through, the agent that took it, the input hashes, the outcome. The deterministic chain lives in the adjacent .provenance.deterministic.json file on disk, pointed to from the XMP under xmp:ProvenanceCompanion.
If you publish PDFs
The MX Compatible badge is not a CogNovaMX trademark or a paid certification. It is the visible mark of an open, verifiable pattern: tagged structure, MX metadata, provenance chain. Any publisher can produce MX Compatible PDFs by adopting the published standard. CogNovaMX operates the reference tooling at cognovamx.com and the registry at reginald.allabout.network; The Gathering at tg.community governs the standard.
Other tools and registries can exist on the same standard. The point of the badge is that the file declares its claim and lets you check it.