"About a quarter of them contained bugs": Gemini 4's ML R&D benchmark

Google DeepMind’s Gemini 4 Argon safety report [1] rules out its “AI automating AI research” thresholds (the ones most relevant to RSI) using a benchmark that, by the report’s own audit, has about a quarter of its scored tasks unpassable. This post dives a bit deeper into the context around this disturbing line tucked away on page 67 of the report. This is no knock on the incredible Google engineers and scientists who worked tirelessly to test Gemini as thoroughly as they could and generate this detailed report. On the contrary, the concern is that these challenges are so difficult that even putting some of the smartest people on the job is not enough, particularly when they are forced to rush (some of the report’s evaluations ran on checkpoints from just 10 and 6 days before the final model, p.160 ). “The Argon safety report is Google’s most thorough by far and IMO there’s a lot of good content, I particularly like the loss of control, alignment & monitorability assessments” — Neel Nanda (@NeelNanda5), X, Oct 13 2026 The ML R&D thresholds and how Google tests them One of the big questions these days with every new frontier AI model is the question of RSI (recursive self-improvement). Google co-founder Sergey Brin, in a roundtable discussion about Argon: “…starting to get onto that RSI treadmill.” “And now our researchers and the model work hand in hand to make the next version of the model.” — Sergey Brin, quoted by Hugh Langley, Business Insider, Oct 15 2026 Google DeepMin…