Continuous diffusion LMs (like ELF) have recently posted new lows on the Gen-PPL leaderboard, as if the low-entropy ceiling of AR decoding has been broken. But arXiv #2607.00588 exposes this leaderboard: Gen-PPL is low not because the model is closer to human speech, but because they're repeating, and Gen-PPL itself is rewarding repetition. After the authors remove ELF-B's repeated n-grams, Gen-PPL jumps from 19.5 to 27.7; the smallest model gets the lowest Gen-PPL, with only one reason — it repeats the most. Ranking continuous diffusion LMs by Gen-PPL is equivalent to reverse sorting: the more you repeat, the higher you rank. Where's the problem? The paper localizes the repetition to a one-dimensional contracting attractor in the self-conditioning feedback loop: each step feeds the clean estimate from the previous step back in, and the model is dragged along this direction toward collapsing to the same token. The failure is one-dimensional, and the fix only needs to be one-dimensional: the authors propose ACE (Attractor-Contrast-Escape), subtracting this unsupervised-estimated direction in each step's feedback. On 105M, a single estimate presses the repetition rate close to human level while maintaining competitive quality; in the same direction it transfers to 342M and 652M with almost no loss, no tuning across samplers; the same recipe can also extract usable directions from other architectures. Since Gen-PPL itself rewards repetition, the paper stops using it for ranking and instead measures the compute needed to produce human-level clean text. Under this metric, ACE is 1.5×–5× cheaper than the comparison. The killing power of this work goes beyond ELF alone — it serves as a warning to all papers and leaderboards that use Gen-PPL as a proxy metric to rank diffusion LMs: before cheering for the leaderboard, first remove the repeated n-grams, then see how much closer the model is to human speech. The honesty of experimental science is hidden in the n-gram deduplication step.