Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI

Current AI systems, even strong reasoners and coders, typically operate within a fixed vocabulary of concepts and fixed success criteria set in advance. This paper argues genuine open-ended intelligence requires systems that can invent, stabilize, and reuse new representational primitives rather than just recombining existing ones. The authors identify two core obstacles - the "vocabulary gap" (inventing new concepts) and "verifier gap" (judging a new concept's value before it's proven useful) - and propose a framework and roadmap involving persistent memory and evolving verification. Applications include guiding future AI research toward genuine scientific discovery, creative innovation, and long-horizon autonomous research capabilities. Authors: Yuan Cao, Haiqian Yang Paper: https://arxiv.org/abs/2607.09560v1