News Summary

• When working with open-weight LLMs like Qwen, controlling refusal behavior on security, administrative, prompts typically requires fine-tuning or permanent weight update.
• Traditional weight abliteration technique neutralizes refusal directions by projecting weight matrices orthogonal to a refusal vector.
• This permanently alters base model weights and can degrade performance across non-refusal tasks also.
• Instead of modifying parameter weights, this approach intercepts intermediate residual streams at runtime across Layers using PyTorch forward hooks.
• We demonstrate this with Qwen3-4B model as Proof of Concept.