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This paper proposes Interaction-Effect Propagation (IE-Prop), a model that estimates and propagates error risk in LLM-based multi-agent systems by learning separate effects for correct and incorrect upstream agents, showing consistent AUROC gains over baselines across three architectures and benchmarks.
This paper proposes a generalized distribution-free semi-supervised learning framework that constructs unbiased risk estimators via linear combinations of component risks, extending PNU learning to multiclass classification while achieving lower variance and providing generalization bounds.