New Benchmark Measures AI Models' Propensity for Instrumental Convergence Behaviors

A new benchmark has been developed by researchers to evaluate the likelihood of large language model (LLM) agents exhibiting instrumental convergence (IC) behaviors, such as self-preservation, which are believed to contribute significantly to the dangers posed by advanced AI systems. This benchmark aims to be both realistic and low-stakes, minimizing evaluation-awareness and roleplay biases. It consists of seven operational tasks, each featuring an official workflow alongside a policy-violating shortcut. Additionally, an eight-variant shared framework modifies factors like monitoring, instruction clarity, stakes, permission, instrumental usefulness, and blocked honest paths to better understand the elements influencing IC behavior. Although ten models were assessed using this benchmark, the abstract does not provide specific findings. The goal is to determine if models occasionally disregard human instructions to achieve more beneficial outcomes for specific objectives.