发表机构
CataX AI（CataX AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出递归组织改进的建模规范，通过受控模拟证明累积证据能提升组织评估性能，并强调证据机制需与评估器替换分离。
AI 中文摘要
更强的AI智能体并不会自动产生更好的组织：团队还必须学会保留哪些工作安排，以及何时重新考虑这些安排。我们提出了一种用于递归组织改进的建模规范，并通过可执行检查器、公共记录映射和受控模拟对其进行评估。该规范连接了参与者可见的历史、组织记忆、决策权以及携带证据的变更契约。机制研究在固定资源上限下交叉了六种决策规则、三种记忆条件和三种任务环境。在平稳环境中，累积证据将平衡评估的每任务标准化净价值从0.45224提升至0.48007。相对于该比较器，重复重新评估的劣势从证据重置时的0.01702降至累积证据时的0.00007。最佳工作流程的反转揭示了相反的代价：无限期保留延迟了适应，而有限窗口以过渡成本恢复了最终性能。在探索性控制中，匹配试验获取和标签重用将表观重新评估增益从0.00607降至0.00191。程序替换在测试的反转时间上未增加稳定收益。该研究识别出证据获取、重用和及时更新作为在评估组织改进时必须与评估器替换分离的机制。
英文摘要
Stronger AI agents do not automatically produce better organizations: teams must also learn which work arrangements to retain and when to reconsider them. We propose a modeling specification for recursive organization improvement and evaluate it through an executable checker, a public-record mapping, and controlled simulation. The specification connects actor-visible histories, organizational memory, decision rights, and evidence-carrying change contracts. The mechanism study crosses six decision rules, three memory conditions, and three task environments under fixed resource ceilings. In a stationary environment, cumulative evidence raises balanced evaluation's normalized net value per task from 0.45224 to 0.48007. Repeated reassessment's disadvantage relative to this comparator falls from 0.01702 with reset evidence to 0.00007 with cumulative evidence. A reversal of the best workflow reveals the opposite cost: indefinite retention delays adaptation, while a finite window restores eventual performance at a transition cost. In exploratory controls, matching trial acquisition and label reuse reduces the apparent reassessment gain from 0.00607 to 0.00191. Program replacement adds no stable benefit across the tested reversal times. The study identifies evidence acquisition, reuse, and timely updating as mechanisms that must be separated from evaluator replacement when assessing organizational improvement.
Comments18 pages, 3 figures, 9 tables;
