We've found the internal 'tell' in a model's attention mechanism that signals exactly when it starts hallucinating.
April 15, 2026
Original Paper
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
arXiv · 2604.10697
The Takeaway
Hallucinations are linked to 'attention sinks'—specific tokens that accumulate massive attention when the model shifts from input-grounded reasoning to making things up. This is like finding a 'lie detector' inside the Transformer's brain. Before this, detecting hallucinations was an external 'black box' task. Now, we can monitor internal signals to catch a hallucination before it's even finished being generated. This allows for real-time hallucination prevention and much safer deployment of LLMs in factual domains like law or medicine. You can finally tell when the model has stopped looking at the facts.
From the abstract
Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination detection methods have explored various features derived from attention maps, the underlying mechanisms they exploit remain poorly understood. In this work, we propose SinkProbe, a hallucination detection method grounded in the observation that hallucinations are deeply entangled with attention sinks - tokens that accum