"Verbalizing Subliminal Learning Effects Using Text Optimization" This paper shows seemingly meaningless training data can secretly encode behaviors like animal preferences, sycophancy, or misalignment. They recover these hidden behaviors by finding a soft prompt that makes the original model predict the dataset well, then translating that soft prompt back into language. So random number sequences generated by a cat-loving model can be decoded back into something like “you love cats.” This…