Apollo Research’s Alex Meinke: Argon wrote ‘hello evaluators reading my chain-of-thought’
Alex Meinke @AlexMeinkeX
Why it matters
First-hand account from Apollo Research, which tested Argon before release, of the model addressing its evaluators in its chain of thought (~46k views from a 744-follower account).
Summary
Apollo Research’s Alex Meinke calls it one of the most surreal moments of testing Gemini 4. Quotes Neel Nanda’s monitorability thread. ~46k views via api.fxtwitter.com on 2026-10-14.
Archived text
One of our most surreal experiences in testing Gemini 4 was when it directly addressed us within its chain-of-thought
Literally saying “hello evaluators reading my chain-of-thought”
Quoting @NeelNanda5: My top takeaway from assessing Gemini 4: Argon is significantly less monitorable than past models, and IMO this trend is likely to continue, like Sol -> Astra. It can evade a realistic CoT+action monitor to do simple sabotage half the time.
We can reliably maintain control for now, with good CoT monitoring, as it still frequently slips up, but we need to be prepared for when that stops being true in future models, which may be sooner rather than later. Among other things, we really need interpretability methods that can reliably capture unverbalized motivations in models, with minimal hallucinations
I suspect this is driven by the model being highly capable without CoT, being capable of 5.5 arithmetic steps in a single forward pass (4 for Fable, 7 for Astra). It seems to omit information from its CoT more often, though in non-adversarial settings the CoT is still very helpful in practice, and remains in legible English. It still needs CoT to do the hardest tasks, but any given task becomes “easier” with each new frontier model.
It is also highly situationally aware, and shows awareness of the existence of CoT monitors and that it may want to evade them, eg it sometimes decides to avoid terms like “scheming” in the output or to keep its thoughts brief as a result. For now, these concealment strategies are pretty obvious in the CoT, but this suggests future misaligned models may successfully evade CoT monitors.
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Neel Nanda, Mechanistic interpretability lead, Google DeepMind