Does Your Large Language Model Have An Intuitive Sense of The Difficulty of A Question?

Difficulty perception is essential for adaptive reasoning in large language models (LLMs). Previous studies rely on training auxiliary models or using extra reasoning rollouts to estimate difficulty, which incurs high computational costs. In this paper, we identify an intrinsic property of LLMs: their internal representations, even before explicit reasoning, encode an informative and useful signal that correlates with problem difficulty. Inspired by this property, we propose Referential Latent Difficulty Perception (RLDP), a training-free and rollout-free method that estimates difficulty directly from hidden activations in a single forward pass, and requires only minimal reference problems. Additionally, we introduce RLDP-AdaSwitch, a lightweight controller that dynamically allocates reasoning effort based on the difficulty signals provided by RLDP, enabling efficient trade-offs between accuracy and compute. Our experimental results across multiple LLMs and diverse datasets, including math reasoning, code generation, and QA, demonstrate that RLDP provides stable and effective difficulty discrimination. This further powers RLDP-AdaSwitch to achieve 1.34×–2.00× efficiency compared to rollout-based methods, while matching the performance of training-based methods.