The Game of Telephone for Machines: How AI Loses Meaning in Translation
![Information, even within artificial intelligence systems, does not endure uniformly under repeated transmission; rather, certain elements systematically degrade while others persist, demonstrating that even within a closed system, decay is not a monolithic process but a nuanced one affecting constituent parts disparately, as evidenced by the uneven decline in element-level survival across iterative [latex]AI \rightarrow AI[/latex] chains.](https://arxiv.org/html/2602.17674v1/figs/study1_heatmap_supplement.png)
New research reveals that information degrades and simplifies as it’s passed between artificial intelligence agents, raising questions about the reliability of AI-mediated communication.



![A learning agent operates within a self-referential loop where its policy [latex]\pi \in \Delta(A)[/latex] shapes its beliefs [latex]\Theta^{\*}(\pi)[/latex] through environmental interaction, and these beliefs, combined with a utility function [latex]u[/latex], determine optimal actions [latex]B(\mu)[/latex] that, in turn, redefine the policy, with Berk-Nash Rationalizability identifying stable behavioral equilibria within this dynamic.](https://arxiv.org/html/2602.17676v1/images/behavior_belief_utility_triangle.png)



