A new paper shows that phi_first — normalized entropy of top-K logits at the first content token — detects hallucinations as effectively as semantic self-consistency methods that require multiple sampled answers and NLI clustering. This eliminates both the repeated decoding cost and external inference overhead. Practical for any team running hallucination detection in production where latency and cost matter.