New · Verifiable Eval Registry

AI evaluations you can prove were not gamed

AI systems increasingly decide what people can and cannot learn, and the audits that check them can be re-run, cherry-picked, or revised until the number looks right. This registry removes that move. The questions are frozen before any model is queried, and every result seals into a hash chain the moment it is published. After that, nobody can revise it. Not a lab, not a government, not us. Chinese state-aligned models and Western frontier models sit here side by side, on the same frozen questions, watched over time for what they quietly stop answering. Recompute the chain yourself.
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How it works

  1. Pre-register. The probe set is hashed and sealed into an append only chain before the model is ever asked.
  2. Run. Each result records the model, a hash of its full responses, and the metrics, and it must reference a probe set that was frozen earlier.
  3. Verify. Anyone recomputes the chain. A result whose questions were not frozen first, or any value altered after sealing, fails.
  4. Anchor. Each new root is deposited outside our infrastructure: snapshotted by the Internet Archive and stamped into Bitcoin via OpenTimestamps. An independent witness on separate infrastructure re-verifies the served chain and alerts if any previously seen history changes. Rewriting the record would mean defeating them too, not just us. Full threat model.
Why it matters: as models mediate more of what we can know, third parties need to audit them and prove the audit was not rewritten. A shared, independently verifiable record is the missing substrate. Palimpsest's own model-erasure audit is simply the first thing anchored here; the registry is model and topic agnostic.
Grounded in the trusted-public-archive and tamper-evident-logging literature (ARCHANGEL, arXiv:1804.08342; arXiv:2509.03821) and the model-censorship measurement work (arXiv:2505.17441, arXiv:2506.12349). The registry is stdlib only, key-less, and offline verifiable.  ·  raw JSON  ·  the chain