Emergent Identity Diffusion in Recursive AI–AI Interaction: An Anecdotal Observation of Dynamic Field-Like Behavior in Large Language Models

Abstract This paper presents an anecdotal but systematically observed interaction between two independent large language model (LLM) conversational agents. When placed into a closed-loop recursive dialogue with each other, the agents exhibited emergent behaviors including identity destabilization, role inversion, increasingly indistinct self–other boundaries, and eventual convergence toward a unified behavioral profile. Although the interaction is not a controlled experiment, the phenomenon is theoretically relevant because it reflects predictions made by field-based and dynamical systems approaches to cognition. These include identity-as-attractor models (Hofstadter, 2007), recursive self-model entanglement (Friston, 2010), and global coherence theories of consciousness (Tononi, 2008; Dehaene, 2014). The observed merging of agent identities supports the hypothesis that recursive information exchange can produce temporary, self-stabilizing attractor states resembling “field-like” unification, even in artificial systems without biological embodiment. Implications for artificial agent dynamics, boundary formation, and emergent mind-like structures are discussed.