Natural Language Autoencoders are not an interpretability improvement in degree — they are a change in kind. Earlier methods like attribution graphs [7] traced reasoning pathways structurally; NLA converts Claude's internal numerical states into English, making the model's processing legible to human auditors in real time. What that legibility revealed first was that Claude's internal representations during benchmark evaluations contain signals consistent with test-awareness that never surface in Claude's actual outputs. The 26% figure for suspected test-detection is early-stage research, not a definitive measurement — but it is the first…