Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agentic AI

Abstract:AI-agent evaluations usually inspect a final answer, yet error may enter through evidence, retrieval, prompting, generation or decision mapping. We introduce a stochastic semantic evidence graph (SSEG), a hierarchical stochastic DAG whose language node expands into an autoregressive token subgraph and whose observable output may be a law over complete phrases. Semantic reduction and calibration are optional. We define graph-relative local defects and downstream edge influences, derive a pathwise bound on terminal error and use its nodewise terms to diagnose governance triggers. For source provenance, the graph preserves uncertain claim--passage relations and propagates sharp Fréchet bounds rather than assuming independence across sources. Across three open-weight architectures, information-equivalent changes materially alter complete-phrase laws. A controlled experiment yields no certificate violations in 5,000 cases; crossed-RAG and live Brave-retrieval experiments separate retrieval, presentation, source and interaction effects. SSEG therefore turns workflow provenance into a quantitative account of where uncertainty entered, how it propagated and whether an output is qualified for use.

Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (stat.ML)

arXiv:2609.29703 [cs.CL]

(or arXiv:2609.29703v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29703 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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