The very first character an AI writes reveals whether it is about to lie to you.
Entropy in the first generated token of a response is a near-perfect indicator of whether an LLM is hallucinating. This discovery means we don't need to generate multiple answers and compare them to see if a model is sure of itself. The model effectively knows it is making things up the moment it starts talking. This single-decode confidence check is just as accurate as expensive, multi-step verification processes used today. It offers a massive shortcut for building faster, more reliable AI assistants that can flag their own mistakes in real-time.
The First Token Knows: Single-Decode Confidence for Hallucination Detection
arXiv · 2605.05166
Self-consistency detects hallucinations by generating multiple sampled answers to a question and measuring agreement, but this requires repeated decoding and can be sensitive to lexical variation. Semantic self-consistency improves this by clustering sampled answers by meaning using natural language inference, but it adds both sampling cost and external inference overhead. We show that first-token confidence, phi_first, computed from the normalized entropy of the top-K logits at the first conten