Researchers investigated how LLMs compute verbal confidence, finding that confidence is cached and retrieved from answer tokens, rather than being generated just-in-time. This suggests that verbal confidence reflects a richer answer-quality evaluation rather than a simple fluency readout, with implications for understanding metacognition in LLMs and improving calibration.