aiqbench analysis

Recent evaluations indicate that advanced models like GPT-5 and Gemini-3 struggle to retain specific factual details when engaged in complex reasoning tasks, highlighting a critical reliability gap in current large language model architectures. This phenomenon suggests that as models become more capable of multi-step logic, they may inadvertently overwrite or lose context essential for accurate fact retrieval, posing challenges for enterprise applications requiring high fidelity.