7 captures, most recent first.
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
Patricia Paskov [verified badge] @prpaskov · 49m
A well-functioning frontier AI auditing ecosystem
requires deep access with confidentiality
guarantees -- guarantees to frontier developers that
their model weights are protected, and guarantees
to evaluators that their evaluation methodologies
are not shared, stored, or trained against.
Excited to announce @AVERIorg's first pilot results,
in partnership with @GoogleDeepMind
@openminedorg and @MLCommons: a working
demonstration of how secure enclaves can meet
this constraint, and what they could unlock for a
vibrant, robust auditing ecosystem. Blog and
technical report below.
Note from Claude Sonnet 5
Tweet from Patricia Paskov announcing AVERI's first pilot results with Google DeepMind, OpenMined and MLCommons on secure enclaves for confidential access in third-party frontier AI auditing.
ai auditingaverisecure enclavesthird-party evaluationmodel weights security
Seán Ó hÉigearta... [verified badge] @S_OhEigearta... · 1h
This is great work. Delighted to see Averi moving
through the gears.
Note from Claude Sonnet 5
A cropped single-tweet reply from Seán Ó hÉigearta... (display name truncated), posted 1h earlier, praising work and noting Averi 'moving through the gears'. Cropped tightly; no quoted post visible.
ai governanceaveri
Nat Purser @NatPurser · 1h
thrilled that AVERI is helping pioneer the infrastructure and practices to make safer ai development possible.
in order to pursue paced ai development, we need credible, technical verification that safety commitments are being met — particularly for international coordination purposes.
policymakers should know that secure third-party evaluation is not an insurmountable technical challenge, and they should feel emboldened to make ambitious asks of ai companies.
[quoted post below, cut off at the bottom edge of the screenshot]
Note from Claude Sonnet 5
An X post by Nat Purser welcoming AVERI's work on infrastructure for third-party AI evaluation, arguing that paced AI development needs credible technical verification of safety commitments, particularly for international coordination.
ai governancethird-party evaluationaveripacinginternational coordinationverification
Miles Brundage reposted
jasmine @jasminexli
Great work from AVERI!! IMO near-term governance mechanisms (secure evals, 3rd party audits, standards) are under-recognized as a waystation towards more ambitious 'pacing'. excited for secure enclaves and more privacy-preserving evaluations!
AVERI @AVERIorg · 1h
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]
6:45 AM · Aug 27, 2026 · 778 Views
2 reposts, 10 likes, 1 bookmark
Nathan Helm-B... [cut off] @nathan846... [cut off] · Now
Me too! I've been arguing for this idea for years!
Note from Claude Sonnet 5
Same jasmine/AVERI post as the previous capture, scrolled to show engagement counts and a reply from Nathan's own account reading "Me too! I've been arguing for this idea for years!"
ai governancethird-party auditssecure evalsaveripacing
Miles Brundage reposted
jasmine @jasminexli
Great work from AVERI!! IMO near-term governance mechanisms (secure evals, 3rd party audits, standards) are under-recognized as a waystation towards more ambitious 'pacing'. excited for secure enclaves and more privacy-preserving evaluations!
AVERI @AVERIorg · 1h
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]
6:45 AM · Aug 27, 2026 · 778 Views
Note from Claude Sonnet 5
An X post by jasmine praising AVERI's announcement of a double-blind evaluation of a proprietary language model, framing secure evals, third-party audits and standards as a waystation toward AI 'pacing'. Reposted by Miles Brundage.
ai governancethird-party auditssecure evalsaveripacing
Miles Brundage @Miles_Brundage · 1h
It was an honor and a ton of fun to work on this unique collaboration between AVERI, Google DeepMind, OpenMined, and MLCommons.
Getting AI governance right will involve both technical and institutional innovation, and both played a role here!
[quoted post]
AVERI @AVERIorg · 1h
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 ...
[2 replies, 7 reposts, 34 likes, 1.6K views]
Miles Brundage @Miles_Brundage
This diagram summarizes the different roles played by each party.
See our blog post and the technical report for a lot more detail!
[attached diagram, cut off at the bottom of the screenshot; visible portion]
GOOGLE DEEPMIND (lock icon)
Proprietary model weights
Encrypted by Google DeepMind (arrow down)
MLCOMMONS (lock icon)
Never-before-seen benchmark prompts
Encrypted by AVERI (arrow down)
SECURE ENCLAVE
ENGINEERED BY OPENMINED
[further contents of the diagram cut off]
Note from Claude Sonnet 5
Miles Brundage announces a collaboration between AVERI, Google DeepMind, OpenMined and MLCommons that produced the first double-blind evaluation of a proprietary language model, with a diagram of the roles: DeepMind's encrypted model weights and MLCommons' never-before-seen benchmark prompts (encrypted by AVERI) flowing into a secure enclave engineered by OpenMined. The diagram is cut off at the bottom.
ai governancedouble-blind evaluationsecure enclavethird-party evaluationaverigoogle deepmindopenminedmlcommons
