TL;DRWe used J-lens on Qwen3.6-27B to find “meta-tokens”: tokens that surface non-obvious computation in the model. When the model reads ambiguous text, 什么意思 ("what does this mean") fires in the J-space, and steering it away makes the model answer "a boiled egg every morning is hard to beat" with nutrition facts instead of catching the pun. On LCM problems, "gcd" fires, pointing at the product/GCD algorithm: swapping the GCD vector from 9 to 3 makes the model change its answer for the LCM of 27 and 90 from 270 to 810. Additionally, before the model hedges, 大概率 ("most likely") fires, and suppressing it makes the model commit to a single option (for e.g. "There are several logical places John could have gone" turns into "he went to the stationery store)." While meta-tokens are hard to find with J-lens’ single token constraint, future methods for multi-token J-lens open the possibility for valuable insights into the model’s internal algorithms.MotivationOne important goal in interpretabil...