# The Geometry of Ignorance: Detecting Suppressed Knowledge in LLMs through Activation Fragmentation

**Authors:** Adar Peleg, Dvir Alsheich, Tomer Ashuach, Avi Mendelson  
**Lab:** Technion – Israel Institute of Technology · **Published:** 3 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.05272) · [PDF](https://arxiv.org/pdf/2611.05272) · [Code](https://github.com/AdarPeleg/geometry-of-ignorance) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.05272.html)

## Abstract

Evaluating whether a large language model (LLM) genuinely knows a concept remains a fundamental problem, since behavioral metrics can be fooled by suppression: a model that refuses to answer may still harbor recoverable knowledge. The framework measures the geometric coherence of \textit{anon-vectors}, the differences in hidden states between a concept-specific query and its anonymized paraphrase, and shows that genuine knowledge forms a tight, low-entropy cluster while ignorance forces geometric fragmentation. Across ten instruction-tuned LLMs from three model families, all fragmentation metrics reliably separate known from unknown concepts, and post-unlearning, fragmentation geometry correlates with adversarial recovery across four attack types and five unlearning algorithms, though the direction of that relationship is shown to depend on attack methodology. Furthermore, a layer-resolved analysis reveals that different adversarial mechanisms exploit this geometry at varying depths, with high attack-specific correlations that are invisible to behavioral evaluation. Together, these results constitute a single-phase, attack-free proxy for unlearning robustness that captures aspects of parametric knowledge inaccessible to output-level metrics. A reference implementation is available at https://github.com/AdarPeleg/geometry-of-ignorance

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