发表机构
The Institute of Energetic Paradigm（能量范式研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可达性诱导优化（RIO）模型，强调AI系统优化声明需明确可达区域，并通过大量基准试验证明优化质量、可达性与控制可靠性需分别报告。
AI 中文摘要
AI系统越来越多地声称能够优化提示词、策略、架构、计划、工具使用轨迹、推理轨迹以及测试时计算。本文认为，除非这些声称明确说明了执行优化的系统实际可达的区域，否则它们就是欠明确的。我们引入了可达性诱导优化（RIO），这是一个模型，其中生成器、验证器、控制器、内存、工具和预算共同诱导出一个可达候选区域。因此，返回的解是一个最佳访问点、一个近似可达最优解，或者仅当额外的证书将可达区域与完整的形式空间联系起来时，才是一个精确的全局最优解。我们证明了可达最优性、虚假全局性、间隙分解、证书、逃逸、剪枝和控制价值等结果。完整的基准记录包含在六个已知最优景观族、七种控制策略、270个景观以及每个景观-方法组合35次运行中执行的66,150次试验。在线附录包括原始试验记录、汇总表、图表、基准代码、验证脚本和校验和。结果表明，控制可以限制、扩展或误导可达性，并且优化质量、可达性质量和控制可靠性必须分别报告。
英文摘要
AI systems increasingly claim to optimize prompts, policies, architectures, plans, tool-use trajectories, reasoning traces, and test-time computation. This paper argues that such claims are underspecified unless they state the region actually reachable by the system that performed the optimization. We introduce Reachability-Induced Optimization (RIO), a model in which a generator, verifier, controller, memory, tools, and budget induce a reachable candidate region. The returned solution is therefore a best visited point, an approximate reachable optimum, or an exact global optimum only when additional certificates relate the reachable region to the full formal space. We prove reachable-optimality, false-globality, gap- decomposition, certificate, escape, pruning, and control-value results. The full benchmark record contains 66,150 executed trials over six known-optimum landscape families, seven control policies, 270 landscapes, and 35 runs per landscape-method. The online appendix includes raw trial records, aggregate tables, figures, benchmark code, validation scripts, and checksums. The results show that control can restrict, expand, or misdirect reachability, and that optimization quality, reachability quality, and control reliability must be reported separately.
