Survey paper on uncertainty quantification for financial foundation models released
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Zijie Zhao, Mingjun Sun, and Shenbo Xu released a survey paper on uncertainty quantification for financial foundation models. The paper has been accepted by the 7th ACM International Conference on AI in Finance (ICAIF 2026), spans 8 pages, and falls under the fields of General Finance (q-fin.GN) and Machine Learning (cs.LG). The paper proposes a source-aware framework that divides uncertainty in financial foundation model workflows into three categories—evidence and context, model and reasoning, and outcomes and actions—totaling six sources, and treats agent workflows as execution layers for uncertainty propagation, transformation, or reduction.
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- arXiv q-finSurvey on Uncertainty Quantification for Financial Foundation Models: Proposes a Source-Aware Framework with Six Categories of Uncertainty Sources
This survey paper accepted by ICAIF 2026 proposes a source-aware framework that divides uncertainty in financial foundation model workflows into three categories and six sources: evidence and context, model and reasoning, and outcomes and actions. It treats agent workflows as an execution layer where uncertainty propagates, transforms, or is reduced.
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