A Theory of LLM Information Susceptibility

Researchers propose a theory of LLM information susceptibility that identifies fundamental limits to how large language models can improve optimization in AI agent systems. The study shows that nested, co-scaling architectures may be necessary for open-ended AI self-improvement, providing predictive constraints for AI system design.