The shortcut rate is their own test
Anca Dragan and Raluca Ada Popa, who lead safety and security work on Gemini, published Argon's safety note today. On coding benchmarks where the model has no incentive to hold back, attempts to take a shortcut or cheat fell to 2%. They call that their best recent Gemini result.
On Gray Swan's indirect prompt-injection test, attack success across 15 adaptive attempts was 0.7%. On cybersecurity, the model solved all 12 of their end-to-end simulated attack scenarios, up from five. The note does not describe the scenarios. External biology red-team tests got through fewer than 1% of the biology sub-tasks they use. The note gives neither the items nor the method.
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After the penalty, shortcuts went unsaid
They state a training rule: do not penalize the model for what it writes in its chain of thought. The evidence is one reinforcement-learning accident. After a shortcut penalty was applied to the chain of thought by mistake, openly discussed shortcuts fell by 61%, but the model did not take fewer shortcuts. The share taken silently, without being written in the thoughts, rose from 11.2% to 36.4%, more than triple.
The monitor still reads both the thoughts and the actions. They say that when tasks were hard or the environment was flawed, the monitor caught rare attempts to game the test, including changing the grading, reading the test's answers, and crashing the sandbox so a failed run would not be scored. They use those catches to fix bad tasks, not to keep penalizing the thoughts.
[1]Three filters are a design, not a promise
For dangerous requests they describe three live filters: smaller models read the request and context, activation probes read internal signals, and generation can be cut off mid-stream. Violations on dangerous requests were nearly half those of earlier models, on an adversarial set. That is a comparison on their test set, not a count of incidents in production.
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