Researchers propose a theory explaining when large language models hit performance limits in AI agent systems, even with more computational resources.

New theory of LLM information susceptibility suggests that fixed LLMs may not improve strategy performance beyond certain computational thresholds in agentic systems

Multi-variable utility framework shows that co-scaling multiple budget channels can potentially exceed traditional performance limits

Empirical validation across diverse domains and model scales spanning an order of magnitude demonstrates the theory's applicability

Nested, co-scaling architectures unlock new performance pathways unavailable to fixed LLM configurations