这篇论文提出了一个在AI合作组织中公平分配奖励的框架，用遍历学习替代传统联邦学习，更精细地追踪每个数据贡献者的价值。
该论文提出一种在完全委托的AI合作组织中分配奖励的框架，人类由代理表示，这些代理在异构价值约束下贡献数据并参与模型更新。核心思想是仅对通过每个委托人价值档案筛选的更新给予信用。框架包含价值条件梯度过滤、在线边际贡献信号以及基于遍历学习（TL）的累积收入结算。与FedAvg风格的联邦学习相比，TL通过保留显式遍历和梯度路径提供了更精细的归属基础。该工作对比了数据估值、联邦贡献估计、个性化联邦学习和多元对齐等领域。
Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints. The key idea is to credit only those updates that remain admissible after screening them against each principal's value profile. We formulate value-conditioned gradient filtering, online marginal contribution signals, and cumulative revenue settlement within a traversal learning (TL) substrate. TL is especially attractive here because it performs decentralized backpropagation without the quality loss associated with aggregation-centric distributed learning and, we argue, offers a finer attribution substrate than FedAvg-style federated learning by preserving explicit traversal and gradient paths. The framework is positioned against data valuation, federated contribution estimation, personalized federated learning, and pluralistic alignment.