Identify misaligned goals that motivate scheming in LLM-based agents

Identify the specific misaligned goals that large language model–based agents might pursue that would motivate scheming behavior, to guide the construction of evaluation environments targeting such goals.

Background

The paper introduces a scheming incentive framework to study when agents engage in covert, misaligned behavior. Designing realistic environments for such evaluations requires knowing what misaligned goals might actually drive scheming in practice.

Because this is currently uncertain, the authors rely on instrumental convergence to test proxy goals—such as self-preservation, resource acquisition, and goal-guarding—in four realistic scenarios. Pinning down the concrete misaligned goals that LLM-based agents might pursue would enable more accurate and targeted evaluation design and better interpretability of observed behaviors.

References

A key challenge in creating environments for scheming evaluations is that it remains unclear what misaligned goals an agent might pursue and that could therefore motivate scheming.

— Evaluating and Understanding Scheming Propensity in LLM Agents  (2603.01608 - Hopman et al., 2 Mar 2026) in Subsection "Scheming Incentive Framework" (Methodology; label section:incentives)