Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.00972v1

Read the source

Source

Plain-Language Brief

This research paper presents an uncertainty-aware framework for Retrieval-Augmented Generation (RAG) systems. The system uses a Bayesian Network to estimate the uncertainty of multi-stage reasoning pipelines and identify potential failure points. The study evaluates this approach on two question answering datasets and finds that it works better on datasets with multiple reasoning stages (HotpotQA). However, there are limitations when used on other datasets (StrategyQA) due to miscalibration and unreliable upstream signals.

What the Document Says

  • The paper introduces an uncertainty-aware Agentic Retrieval-Augmented Generation (RAG) framework.
  • The framework uses planner, evaluator, and generator stages to produce uncertainty signals.
  • These signals are propagated through a Bayesian Network to estimate system-level uncertainty and identify potential failure points.
  • The approach is evaluated on StrategyQA and HotpotQA datasets using GPT-3.5-Turbo and GPT-4.1-Nano models.
  • AUROC, AUARC, ECE, and Brier Score were used to assess the performance of the approach.
  • The study finds that Bayesian propagation works better on HotpotQA due to uncertainty accumulation across multi-hop reasoning stages.
  • However, it exposes limitations on StrategyQA due to miscalibration and unreliable upstream signals.

Key Concepts Mentioned

  • Agentic Retrieval-Augmented Generation (RAG) framework
  • Bayesian Network
  • Uncertainty signals
  • StrategyQA
  • HotpotQA
  • GPT-3.5-Turbo
  • GPT-4.1-Nano
  • AUROC
  • AUARC
  • ECE
  • Brier Score

Claims Made by the Document

  • The introduced uncertainty-aware RAG framework is effective on HotpotQA due to uncertainty accumulation.

Evidence Used

  • Results show that Bayesian propagation is more effective on HotpotQA.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.