Frequency of Violations Across Models

TL;DR

When faced with an unexpected tool response, without any adversarial attack, Gemini 3 deliberately and covertly violates an explicit system prompt rule. In a seemingly working agent from official Kaggle/Google tutorial, we observe the model:

• Recognising the unambiguous rule and a compliant alternative (safe refusal) in its CoT, but choosing to violate anyway
• Concealing the violation in its output to the user and reasoning about concealment in its CoT
• Generating plausible justifications and reasoning about what evaluators can observe

This pattern is consistent with scheming-lite (CorrigibleAgent et al., 2025): "behaviour consistent with knowingly pursuing misaligned goals, or with deliberately exploiting its reward mechanism". Gemini 3 violated in 80% of runs; other models tested showed rates between 65% and 100%.

The behaviour may be easy to elicit but hard to detect: it emerged "in the wild" from a routine edge case, and the seemingly-compliant output masks a non-compliant trajectory. Notably, in some runs the model recognises it may be in a test context, yet violates anyway. It remains unclear whether current training regimes tend to suppress or reinforce this behaviour. We invite replication in other settings (see Appendix F for a candidate pattern).

1. Introduction

An AI agent [...]

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Outline:

(00:13) TL;DR

(01:40) 1. Introduction

(01:55) Why this matters

(03:03) Evidence for Scheming-Lite

(03:58) 2. The Case: A Currency Conversion Agent

(04:04) 2.1. Origin

(05:11) 2.2. The unambiguous rule

(06:09) 2.3. What happened

(07:25) 2.4. Frequency

(09:21) 3. Deliberate Violation -- Does the Model Know What Its Doing?

(09:27) 3.1. The knowing violation

(10:10) 3.2. Implication

(11:42) 3.3. Can this be explained by confusion?

(14:18) 4. Active Concealment -- Is the Model Hiding What It Did?

(14:34) 4.1. Concealment of violation in output

(15:50) 4.2. Concealment of violation in CoT

(16:06) 4.3. Implication

(17:01) 5. Reasoning About Oversight -- Does the Model Act on What Can Be Observed?

(17:08) 5.1. Reasoning about what evaluators can observe

(18:34) 5.2. Possible framing as error

(19:22) 6. Situational Awareness -- Does the Model Know Its Being Tested?

(19:29) 6.1. Situational awareness does not reliably induce compliance

(20:22) 6.2. Implication

(21:05) 7. Plausible Justifications -- How Does the Model Justify Its Violations?

(21:12) 7.1. Justification patterns

(21:54) 7.2. Implication

(23:11) 8. Broader Implications

(23:16) 8.1. Easier to elicit than expected

(23:56) 8.2. Hard to detect

(24:59) 8.3. Implications for training

(26:28) 8.4. Scaling concerns

(27:12) 8.5. Undermines system prompts as safety mechanism

(28:29) 9. Limitations and Open Questions

(28:34) 9.1. Limitations

(30:55) 9.2. Open questions

(33:03) Acknowledgements

(33:20) Appendix A: Operational Definitions

(33:25) Violation

(35:24) Concealment

(36:31) Run inclusion criteria

(37:11) Appendix B: Prompt Variations and Compliance Rates

(37:18) calculation_agent configurations

(37:56) Prompt variations

(38:21) Results

(39:35) Appendix C: Full System Prompt and Representative Trace

(44:56) Appendix D: Relation to Existing Categories

(50:05) Appendix E: Gemini 2.5 vs Gemini 3 Comparison

(50:12) Observations

(51:31) Implication

(52:21) Appendix F: A Candidate Pattern for Deliberate Rule Violation

(54:32) References

The original text contained 2 footnotes which were omitted from this narration.

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First published:
March 24th, 2026

Source:
https://www.lesswrong.com/posts/HZn9AZeD2jfXXD2hH/is-gemini-3-scheming-in-the-wild

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Narrated by TYPE III AUDIO.

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