Google Research published a paper that studies how to make generative AI systems produce answers that do more than sound plausible. The researchers say that their ALDRIFT framework “opens exciting avenues” for moving beyond answers that merely have a high probability. The paper, titled “ Sample-Efficient Optimization over Generative Priors via Coarse Learnability ,” examines a problem in which generated answers must remain likely under a model while also moving toward a separate goal. The research points toward new avenues for addressing the AI plausibility trap. Google ALDRIFT The evidence in the paper centers on a framework called ALDRIFT (Algorithm Driven Iterated Fitting of Targets). The method repeatedly refines a generative model toward lower-cost answers and uses a correction step to reduce accumulated error during the process. The paper also introduces “coarse learnability.” The term means the learned model does not need to perfectly match the ideal target.…