智能体AI的验证与自我改进：基础与极限
原标题：Verification and Self-Improvement in Agentic AI: Foundations and Limits
AI 摘要
该论文为智能体AI的验证与自我改进建立形式化框架，区分搜索更久、获得额外支持、修改提议与验证机制这三类性能提升来源。作者用带隐藏终端随机性的有界验证定义“阶段”，证明独立多数放大保持语言不变，而随机磁带上的存在性接受可能引入错误输出；并指出在公共可靠解释器与固定验证协议下，一致有界的自我修改仍停留在同一验证类内。框架将自我改进主张与正确性、可采纳证据、验证资源和选择误差等义务绑定。
正文节选
Verification and Self-Improvement in Agentic AI: Foundations and Limits Abstract Agentic AI systems can improve by searching longer, receiving additional support, or modifying how they propose and verify outputs. A performance score does not distinguish these mechanisms. We compare these changes through bounded verification with hidden terminal randomness. A stage specifies admissible transcripts, polynomial bounds, an alternating verification protocol, and a terminal checker. Its native reach u