Middle Intelligence Trap

The "middle intelligence trap" describes a phenomenon where large language models (LLMs), despite possessing considerable knowledge, struggle with complex reasoning tasks due to limitations in compositional abilities and susceptibility to cognitive biases like the representativeness heuristic. Current research focuses on understanding these limitations, particularly within the context of reasoning under uncertainty and the impact of training methodologies (e.g., next-token prediction vs. autoregressive blank infilling) on model performance. Addressing these weaknesses is crucial for improving LLM reliability and trustworthiness in real-world applications, requiring advancements in both model architecture and training techniques.