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Detecting Suppressed Knowledge in LLMs via Activation Fragmentation

This paper introduces a framework that measures the geometric coherence of hidden-state differences between concept-specific queries and anonymized paraphrases to detect whether an LLM genuinely knows a concept. It shows that genuine knowledge forms tight clusters while ignorance causes fragmentation, and that this geometry correlates with adversarial recovery after unlearning across multiple models and attack types. The method offers a single-phase, attack-free proxy for unlearning robustness that captures knowledge inaccessible to output-level metrics.

Why it matters: Provides a novel, attack-free method to evaluate LLM knowledge and unlearning robustness, with empirical validation across multiple models and attacks.