Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives

As AI systems increasingly act as proxies for human stakeholders in shared learning environments, a thorny question arises: how do you fairly reward each participant's contribution when different contributors have different values — and when some contributions might violate those values? This paper proposes a framework that filters gradient updates by each principal's value profile before computing credit, grounded in a "traversal learning" substrate that preserves richer attribution paths than standard federated averaging. Relevant to decentralized AI training cooperatives, privacy-preserving machine learning, pluralistic AI alignment efforts, and emerging contexts like data DAOs where multiple parties co-own and co-train models under heterogeneous ethical constraints. Authors: Young Yoon, Jimin Kim, Soyeon Park Paper: https://arxiv.org/abs/2606.28217v1