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

Paper recorded by Signals 4 on 2026-11-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.AI · 人工智能 · first seen 2026-11-04

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,

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