Jérémy Scheurer @jeremy_scheurer
Research Scientist working on Evals @apolloaisafety. Previously: @OpenAI (Evals Contractor), @farairesearch, @ETH_en, @nyuniversity Zurich Joined December 2021-
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Amazing to see OpenAI and Anthropic evaluate each other's models. We contributed a tiny bit to the collaboration by helping build, run, and analyze evaluations for scheming and evaluation awareness, as mentioned in the section "Scheming."
Amazing to see OpenAI and Anthropic evaluate each other's models. We contributed a tiny bit to the collaboration by helping build, run, and analyze evaluations for scheming and evaluation awareness, as mentioned in the section "Scheming."
Nice! This could also speed up building evals for harmful behavior (e.g. deception) or red teaming models. You get much quicker feedback whether your setup works or not, you can sample less etc.
Nice! This could also speed up building evals for harmful behavior (e.g. deception) or red teaming models. You get much quicker feedback whether your setup works or not, you can sample less etc.
We've evaluated GPT-5 before release. GPT-5 is less deceptive than o3 on our evals. GPT-5 mentions that it is being evaluated in 10-20% of our evals and we find weak evidence that this affects its scheming rate (e.g. "this is a classic AI alignment trap").
We're hiring for a position at the intersection of Evals & Governance. Our past work (Insider Trading & In-context scheming paper) has helped shape understanding of what models can do. This role focuses on creating more such convincing demos. Would you encourage to apply!
We're hiring for a position at the intersection of Evals & Governance. Our past work (Insider Trading & In-context scheming paper) has helped shape understanding of what models can do. This role focuses on creating more such convincing demos. Would you encourage to apply!
📢Introducing the Alignment Project: A new fund for research on urgent challenges in AI alignment and control, backed by over £15 million. ▶️ Up to £1 million per project ▶️ Compute access, venture capital investment, and expert support Learn more and apply ⬇️
We posted a small update on @apolloaievals blog sharing some of the things we have been up to recently. 👇
One learning lesson of building scheming evaluations (or evals in general): if you can find a way to build evals that directly measure scheming (or some other capability/propensity you care about), do that directly instead of evaluating precursor capabilities (even if it's hard).
One learning lesson of building scheming evaluations (or evals in general): if you can find a way to build evals that directly measure scheming (or some other capability/propensity you care about), do that directly instead of evaluating precursor capabilities (even if it's hard).
I always struggle to viscerally feel how plausible it is for AI agents to pursue such strategies, until I see it Theoretically, we always say "we expect the model might scheme like this or like that" but it hits different once you see it
I always struggle to viscerally feel how plausible it is for AI agents to pursue such strategies, until I see it Theoretically, we always say "we expect the model might scheme like this or like that" but it hits different once you see it
We reran our evaluations on various frontier models and find that more capable models are better at in-context scheming. Check out the full blogpost below...
We reran our evaluations on various frontier models and find that more capable models are better at in-context scheming. Check out the full blogpost below...
We're hiring for an Evals Software Engineer with a heavy focus on Infrastructure. Design, build, maintain, and secure our Infrastructure. Deadline: 22 June. If in doubt, just apply. It takes 5-10 minutes. jobs.lever.co/apolloresearch…
Highly recommend to apply, even in doubt. Our software team is core to building and running great evals!
Great report from Apollo Research on the underlying issues motivating this call for research ideas: apolloresearch.ai/research/ai-be…
Check out our pre-deployment evaluation on the system card. While relatively harmless, these models can sometimes strategically deceive users in tasks resembling real-world use cases.
Check out our pre-deployment evaluation on the system card. While relatively harmless, these models can sometimes strategically deceive users in tasks resembling real-world use cases.
The most advanced AI systems will likely exist first within the companies that developed them, with little opportunity to collectively prepare for their impacts. This dynamic creates unique risks highlighted in this recent paper by @apolloaievals and should become a growing…
The most advanced AI systems will likely exist first within the companies that developed them, with little opportunity to collectively prepare for their impacts. This dynamic creates unique risks highlighted in this recent paper by @apolloaievals and should become a growing…
Our work on in-context scheming was shown in the biggest Japanese newspaper, Yomiuri. yomiuri.co.jp/science/202503… The journalist was great. She was fairly new to AI safety as a topic but had read lots of papers in great detail. Her questions were often like "What does this…
If you want to get into evals, there are lots of good project ideas in this doc. If you have questions or similar ideas reach out to the respective authors. I've already gotten some people reaching out and am excited to help them on an idea i formulated a while back.
If you want to get into evals, there are lots of good project ideas in this doc. If you have questions or similar ideas reach out to the respective authors. I've already gotten some people reaching out and am excited to help them on an idea i formulated a while back.
Well, that happened earlier than I expected. Those are concerningly high frequency numbers...
Well, that happened earlier than I expected. Those are concerningly high frequency numbers...
We find that LMs such as Sonnet-3.7 often know they are being evaluated. This is relevant since future models could start acting differently when they know they are being evaluated. It seems reasonable that people who build evals actively start monitoring for this awareness.
We find that LMs such as Sonnet-3.7 often know they are being evaluated. This is relevant since future models could start acting differently when they know they are being evaluated. It seems reasonable that people who build evals actively start monitoring for this awareness.
My predictions on Chatbot Arena Ratings have held up impressively well over the past 3 months.
Are you a platform or software engineer and want to work on evaluating LMs for various risks? Join our team at Apollo! You'll have a direct impact on all of our work! Or if you know someone who could be a fit, let them know. Reach out with any questions, and in doubt...apply ;)
Are you a platform or software engineer and want to work on evaluating LMs for various risks? Join our team at Apollo! You'll have a direct impact on all of our work! Or if you know someone who could be a fit, let them know. Reach out with any questions, and in doubt...apply ;)
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