发表机构
MIT（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究指出当前人工智能系统虽能力强但表示框架固定，构建开放式智能需新操作。通过词汇和验证差距刻画与真正开放式智能的距离，基于智能行为的认知转换区分不同类型转换，进而提出创新自主性阶梯及推进开放式人工智能的方向。
AI 中文摘要
现代人工智能系统正越来越多地根据其推理、编码、证明定理、使用工具以及长期研究任务的能力进行评估。这些能力强大，但存在结构限制，模型运行的表示框架通常是预先固定的。本文认为构建能够开放式创新的更强智能系统需要新的操作类别，即创建、稳定和重用新的表示原语。通过词汇差距（发明和稳定新表示原语的困难）和验证差距（判断新原语价值的困难）来刻画当前人工智能系统与真正开放式智能的距离。通过将智能行为视为认知转换序列，区分了固定表示框架内的空间内转换和可能修改框架本身的生成转换。在此基础上，提出了创新自主性阶梯并概述了推进开放式人工智能的几个方向。
英文摘要
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 searched rather than simply searching within it. We characterize the distance between current AI systems and genuinely open-ended intelligence through two gaps. The first is the vocabulary gap, the difficulty of inventing and stabilizing new representational primitives rather than merely recombining existing ones. The second is the verifier gap, the difficulty of judging the value of a new primitive when its full payoff may be visible only after future reuse. We interpret both gaps through a unified framework of intelligence as cognitive discrepancy reduction. By viewing intelligent behaviors as a sequence of cognitive transformations, we distinguish intra-space transformations which operate within a fixed representational frame, from generative transformations which may modify the frame itself. On this basis, we propose a ladder of innovation autonomy and outline several directions for advancing open-ended AI, including objectives that reward useful representational change, persistent memory architectures for invented primitives, and adaptive verification mechanisms capable of evolving alongside the representations they evaluate.
