CHIVE tool finds LLM interpretability methods don't predict behavior

Researchers introduced CHIVE, an agentic pipeline that discovers unexpected LLM (AI language model) behaviors in real-world settings and explains them through counterfactual prompt edits. When evaluated, activation-reading interpretability tools—which aim to understand how AI models work internally—provided no improvement: agents using these tools predicted experiment outcomes no better than agents that simply read the transcript.