In the news
The Geometry of Ignorance: Detecting Suppressed Knowledge in LLMs through Activation Fragmentation
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers developed a method to detect whether LLMs truly know suppressed information by analyzing geometric patterns in hidden activation states.
- Why it matters
- Matters for engineers building safety systems, evaluating unlearning effectiveness, and assessing whether model refusals hide recoverable knowledge.
- Watch out
- The correlation between fragmentation geometry and adversarial recovery depends on attack methodology, so results may not generalize across all attack types.
- llm
- language model
- rag
- edge
- eval
The patterns behind this
- Eval-Driven Development (Agent CI)
- Temporal Knowledge Graph Memory
- Constitutional AI Evaluation Framework
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.