Yusen Peng and Shuhua Mao think AI hallucinations might actually be features, not bugs:

As generative systems evolve, the question may no longer be how to suppress all hallucinations, but rather, how to recognize and refine the meaningful ones. In doing so, we open a path not just to generation, but to genuine creative evolution.

We've been treating AI hallucinations as bugs to squash. Errors, inconsistencies, unexpected outputs - all seen as failures to be eliminated through better alignment.

But what if we're throwing away the source of genuine creativity? What if those "errors" are actually where the interesting stuff happens?

This paper proposes a radical shift in perspective. It introduces a framework that, instead of suppressing unexpected outputs, actively seeks them out and treats them as raw creative material. The idea is to systematically generate deviant results, amplify their most promising aspects, and then refine them through a structured pipeline that includes human feedback. It’s a move away from designing for flawless execution and towards designing for productive imperfection.

How do you identify which hallucinations contain creative potential and which are just nonsense? How do you build tools that can spot the difference between interesting mistakes and useless ones?