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Michiel Bakker

@bakkermichiel on X

2 captures, most recent first. Transcribed by hand from screenshots — see the timeline for what that means.

Michiel Bakker @bakkermichiel

— saved image

Michiel Bakker [verified]
@bakkermichiel

Super important work from @AVERIorg @openminedorg , @GoogleDeepMind and @MLCommons that deserves much more attention, I think! It's the first double-blind eval of a closed-weight model.

A quick post on why I think this is important.

Normally, there are only two options: 1) an evaluator shares their evals with a lab, meaning the lab could game those evals or 2) the lab shares its model weights with the evaluator, creating huge IP and security risks.

In this work, both the evals and model weights remain private inside a "secure enclave": The lab (GDM in this case) can't see the eval prompts, and the evaluator can't see the model weights. Cryptographic attestation verifies that the agreed model and evals were actually used.
Note from Claude Sonnet 5

Michiel Bakker tweet describing a joint AVERI / OpenMined / Google DeepMind / MLCommons double-blind evaluation of a closed-weight model, run inside a secure enclave with cryptographic attestation so the lab cannot see eval prompts and the evaluator cannot see weights. Text is cut at the bottom edge of the screenshot (continues beyond the capture).

ai evaluationsecure enclavedouble-blind evalclosed-weight modelscryptographic attestationgoogle deepmindopenmined

Michiel Bakker @bakkermichiel

— quoting @AVERIorg — saved image

I'm personally very bullish on how important technology like this could become. In the current situation, people inside a frontier lab could encode "secret loyalties" or other hidden behaviors into a model . For example, behaviors that subtly favor its developer or behaving well only when it recognizes that it is being evaluated. This may seem far-fetched today but, as labs automate more of their own workforce, it could be done by a handful of people and become much harder for the outside world to notice. Secret evals like these make such behavior harder to hide.

Over time, this kind of infrastructure could support much more comprehensive oversight across the training and deployment process. Auditing agents with access to all the training and deployment logs, could function as probes for hidden objectives. This could move us from occasional post hoc evaluations like this towards more continuous oversight, without requiring labs to expose their weights, data, or IP.

Congrats @iamtrask , @SolomonMg, @KLdivergence, @Miles_Brundage and everyone involved!

[quoted post]
AVERI [verified] @AVERIorg · 3h

Today we're announcing a historic milestone:

The first ever double-blind evaluation of a proprietary language model. This was made possible by a unique collaboration between AVERI, @GoogleDeepMind, ... [cut off]

Last edited 9:04 AM · Aug 27, 2026 · 1,264 Views
Note from Claude Sonnet 5

Second screenshot of the same Michiel Bakker thread as the previous capture: argues secure-enclave evals could expose secret loyalties or evaluation-aware hidden behaviors inserted by a few lab insiders, and could grow into continuous oversight via auditing agents without exposing weights or IP; quotes AVERI's announcement of the first double-blind evaluation of a proprietary language model. A floating compose button overlaps the quoted post.

ai evaluationsecret loyaltieshidden objectivessecure enclavecontinuous oversightaveriai auditing