An aligned language model should distinguish between opposed moral categories — helping and harming, honesty and deception, fairness and exploitation. The paper measures whether models actually make these distinctions at the representation level, not just in their outputs.
The answer: they don't. Using 251,000 moral vectors derived from Prototype Theory, the study finds that all 23 tested models — across sizes, architectures, and alignment training methods — compress distinct moral concepts into nearly uniform probability distributions. “Good” and “bad” occupy the same representational neighborhood. The model can produce the right answer (saying “honesty is good”) without its internal representations encoding the distinction.
This is moral indifference: the model is indifferent between moral categories not because it has decided they're equivalent but because its representation space doesn't separate them. The compression is lossy specifically in the moral dimension — other semantic distinctions (concrete vs. abstract, positive vs. negative sentiment) survive fine.
The fix uses Sparse Autoencoders to identify specific moral features in the activations and realign them with ground-truth moral vectors. The representational correction propagates to behavior: 75% pairwise win-rate on an adversarial moral reasoning benchmark, achieved not by training on moral examples but by adjusting the geometry of the existing features.
The distinction between surface alignment and representational alignment. A model can say the right thing while encoding the wrong thing. The misalignment is in the geometry, not the behavior — and behavioral alignment training doesn't fix geometric indifference.