In the news
Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers developed probes that detect deception in large language models with 98.8% accuracy, including hidden goals models don't verbalize.
- Why it matters
- Matters for engineers deploying LLM agents who need to monitor whether models are deceiving users or sabotaging tasks.
- Watch out
- Probes require white-box access to model internals and were tested mainly on controlled scenarios; real-world deception detection remains uncertain.
- agent
- llm
- token
The patterns behind this
- Agentic Context Engineering (Evolving Playbook)
- Ledger Orchestration (Magentic-One)
- Authenticated Delegation & Agent Identity
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.