Actionable Activation Directions for Detecting and Mitigating Emergent Misalignment Across Language Model Families

arXiv:2606.20225v1 Announce Type: new Abstract: Fine-tuning language models on insecure code induces emergent misalignment with poorly understood internal structure. We investigate whether this misalignment corresponds to a causally actionable activation-space direction shared across architectures. Across four instruction-tuned model families (Qwen2.5-1.5B, Gemma-2-2B, Llama-3.2-1B, Ministral-3-3B) finetuned identically, a difference-in-means direction achieves 99.6% separation of aligned and misaligned activations at each model's final layer. Causal steering by subtracting this direction redu

Source: arXiv cs.CL — read the full report at the original publisher.

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