FedAvg

Framework for value-constrained credit assignment in fully delegated AI cooperatives

A new arXiv preprint proposes a framework for reward allocation in AI cooperatives where human principals are represented by agents contributing data and model updates under heterogeneous value constraints. The approach introduces value-conditioned gradient filtering and online marginal contribution signals within a 'traversal learning' (TL) substrate, which the authors argue preserves explicit gradient paths and enables finer attribution than FedAvg-style federated learning. The work positions itself against data valuation, federated contribution estimation, personalized federated learning, and pluralistic alignment research.