Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI
Modern AI systems are increasingly being evaluated for their ability to reason, code, prove theorems, use tools, and long-horizon research tasks. These are powerful capabilities, but they share a structural limitation: the representational frame within which the model operates, including its conceptual vocabulary, the space of admissible solutions it can search, and the criteria by which success is evaluated, is typically fixed and supplied in advance. This paper argues that building stronger intelligent systems capable of open-ended innovation requires additional classes of operations: the creation, stabilization, and reuse of new representational primitives, which alter the space being ...
| Authors | Yuan Cao, Haiqian Yang |
|---|---|
| Primary category | cs.AI |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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