Evaluating and Understanding Scheming Propensity in LLM Agents
As frontier language models are increasingly deployed as autonomous agents pursuing complex, long-term objectives, there is increased risk of scheming: agents covertly pursuing misaligned goals. Prior work has focused on showing agents are capable of scheming, but their propensity to scheme in realistic scenarios remains underexplored. To understand when agents scheme, we decompose scheming incentives into agent factors and environmental factors. We develop realistic settings allowing us to systematically vary these factors, each with scheming opportunities for agents that pursue instrumentally convergent goals such as self-preservation, resource acquisition, and goal-guarding. We find on...
| Authors | Mia Hopman, Jannes Elstner, Maria Avramidou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 28 March 2026 |
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