Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering

2026-07-01

AI Summary

This work addresses the lack of reliable uncertainty estimation for reasoning failures in agentic retrieval-augmented generation (RAG) systems during multi-hop question answering. It proposes the first uncertainty-aware agentic RAG framework, which integrates semantic disagreement metrics with a generator self-evaluation mechanism to produce stage-wise uncertainty signals. For the first time, a Bayesian network is employed to propagate uncertainty across the system and identify failure-prone components. Experimental results on StrategyQA and HotpotQA demonstrate that the proposed framework significantly improves AUROC and AUARC while reducing ECE and Brier Score, effectively modeling uncertainty accumulation in multi-hop reasoning and enabling both node-level failure warnings and system-level confidence assessment.

Abstract

Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines may fail. This paper presents an uncertainty-aware Agentic Retrieval-Augmented Generation (RAG) framework in which planner, evaluator and generator stages produce uncertainty signals derived from semantic divergence and generator self-evaluation. These signals are propagated through a Bayesian Network (BN) to estimate system-level uncertainty and provide node-level indicators of potential failure points across the workflow. The approach is evaluated on StrategyQA and HotpotQA using GPT-3.5-Turbo and GPT-4.1-Nano, with Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Accuracy-Rejection Curve (AUARC), Expected Calibration Error (ECE), and Brier Score used to assess discrimination, selective prediction and calibration. Results show that Bayesian propagation is more effective on HotpotQA, where uncertainty accumulates across multi-hop reasoning stages, while StrategyQA exposes limitations caused by miscalibration and unreliable upstream signals. The study positions Bayesian uncertainty propagation as a promising but preliminary mechanism for monitoring Agentic RAG systems, with future validation required in industrial domains such as Offshore Wind (OSW) maintenance decision support.

Problem

• Agentic RAG
• Uncertainty Propagation
• Multi-Hop Question Answering
• Failure Detection
• Trustworthy AI

Innovation

• Bayesian Uncertainty Propagation
• Agentic RAG
• Multi-Hop Question Answering
• Uncertainty Quantification
• Bayesian Network