Understanding Cognition-Induced Risks in Agentic AI Systems

A new arXiv paper (2608.15304v1) systematically analyzes risks induced by expanding cognitive capabilities in frontier agentic systems powered by large language models (LLMs), proposing a three-level framework from physical to social to self-referential cognition. The authors identify potential risks to human agency, autonomy, and control capability at each level and propose mitigation strategies to enhance controllability and ensure long-term safe development.

arXiv:2608.15304v1 Announce Type: new Abstract: Frontier agentic systems powered by large language models LLMs exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their cognitive engagement raises critical concerns for human society that remain insufficiently studied. To address this gap, we systematically analyze risks induced by expanding cognitive capabilities, following a three-level framework defined by their cognitive scope, from physical cognition to social cognition, and finally to self-referential cognition. We study their potential risks to human agency, autonomy, and control capability, corresponding to each cognitive level. We finally propose strategies to mitigate these risks and enhance the controllability of agentic AI systems, ensuring their long-term safe development.