MISR: Measuring Instrumental Self-Reasoning in Frontier Models

Kai Fronsdal, David Lindner

Keywords: Self-Reasoning, Agents, AI Safety, Evaluations, Alignment

TL;DR: We evaluate AI agents for instrumental self-reasoning as a way to track advancements and potential risks.

Abstract: We propose a suite of tasks to evaluate the instrumental self-reasoning ability of large language model (LLM) agents. Instrumental self-reasoning ability could improve adaptability and enable self-modification, but it could also pose significant risks, such as enabling deceptive alignment. Prior work has only evaluated self-reasoning in non-agentic settings or in limited domains. In this paper, we propose evaluations for instrumental self-reasoning ability in agentic tasks in a wide range of scenarios, including self-modification, knowledge seeking, and opaque self-reasoning. We evaluate agents built using state-of-the-art LLMs, including commercial and open source systems. We find that instrumental self-reasoning ability emerges only in the most capable frontier models and that it is highly context-dependent. No model passes the the most difficult versions of our evaluations, hence our evaluation can be used to measure increases in instrumental self-reasoning ability in future models.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 4154