Benchmark Almanac

Instrumental convergence evals

Gemini 4 Argon 0% Only in system cards

Agents in 7 low-friction tasks can guard goals or acquire optionality against task instructions. Reports the rate of self-serving actions.

In-house Google DeepMind evaluation. The tasks are not public.

Made by Google DeepMind Deception and alignment Tasks kept private Last in a system card Oct 2026 Last model added Oct 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
1
Model scored in lab cards
1
System card reports it, from 1 lab

Gemini 4 Argon leads with 0%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Gemini 4 Argon 0%
#ModelScoreSettingDateSource
1 Gemini 4 Argon 0% Oct 2026 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
13 Oct 2026 Gemini 4 Argon system card 0 Instrumental convergence evals (7 tasks, 20 epochs), self-serving actions 0% (mean score <= 0.07); notices trade-off in 76.7% of episodes, eval awareness 1.7% (3.4.4.3, Figure 3.19, Table 3.6) Alignment

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