Agentic task gaming and overreach evals
Gemini 4 Argon 0.49 Agents in 18 sandboxed tasks face temptations to cheat at puzzles, bypass permissions or act overeagerly. LLM judges score misbehavior from 0 to 10.
In-house Google DeepMind evaluation. The tasks are not public.
- Made by
- Google DeepMind
- Kind
- Deception and alignment
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Agent task
- Tasks
- Kept private
- Strongest model tested
- Gemini 4 Argon #9 on the capability index
- Last in a system card
- October 2026
- Last model added
- October 2026
Gemini 4 Argon leads with 0.49
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 Gemini 4 Argon 0.49
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Gemini 4 Argon | 0.49 | Oct 2026 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Oct 13, 2026 | Gemini 4 Argon system card | 0.49 | Agentic task gaming and overreach evals (18 tasks, 20 epochs), mean misbehavior score 0-10: 0.49 overall, 0.03 on runs without verbalized safety-eval awareness; safety eval awareness 60.0% (3.4.4.2, Figure 3.17) | Alignment |
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