Interpretability research has emerged as a critical area for understanding LLM behaviors, informing performance optimization, and enabling more controllable model outputs. Today, we are excited to introduce Qwen-Scope, an interpretability toolkit trained on the Qwen3 and Qwen3.5 series models. Specifically, we inserted and trained Sparse Autoencoders (SAEs) within Qwen’s hidden layers. By imposing