Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives

2026-06-26

AI Summary

This work addresses the challenge of achieving fair reward allocation and precise contribution attribution in fully delegated AI collaborative systems under heterogeneous human value constraints. The authors propose a value-constrained credit assignment framework that leverages value-conditioned gradient filtering and traversal-based learning to enable fine-grained attribution while preserving explicit gradient pathways. By integrating online marginal contribution signals with cumulative payoff settlement, the method effectively supports efficient collaboration among agents with diverse value preferences. Experimental results demonstrate that the proposed framework significantly outperforms conventional federated averaging approaches, maintaining model performance while avoiding the quality degradation commonly associated with aggregated learning.

Abstract

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.

Problem

• credit assignment
• value constraints
• AI cooperatives
• reward allocation
• federated learning

Innovation

• value-constrained credit assignment
• traversal learning
• gradient filtering
• marginal contribution
• decentralized backpropagation