Large reasoning models improve problem-solving performance by generating extended Chain-of-Thought, but often suffer from overthinking, producing unnecessarily long and redundant reasoning traces. Prior works mainly focus on directly compressing reasoning length, or selecting reasoning modes globally based on task difficulty, without accounting for the dynamic and heterogeneous nature of the reasoning process itself. This lack of consideration for inference-time diversity limits their ability to adapt reasoning strategies flexibly. To address this gap, we propose Interleaved System-1/2 Reasoning (InterSR), a cognitively inspired framework that dynamically alternates between System 1 (fast) and System 2 (slow) reasoning modes during generation. Central to InterSR is a learnable system switching signal that reflects the necessity for deeper reasoning at each intermediate step and enables fine-grained control over cognitive mode transitions. Experiments across mathematical and cross-domain benchmarks show that InterSR effectively captures internal reasoning signals to reduce token usage while maintaining strong and robust accuracy. We release our code at https://github.com/buaadh/InterSR.