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Jiang, M., Huang, T., Guo, B., Lu, Y. and Zhang, F. (2025) Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information. In: Mahmud, M., Doborjeh, M., Wong, K., Leung, A.C.S., Doborjeh, Z. and Tanveer, M., Eds., Neural Information Processing. ICONIP 2024. Communications in Computer and Information Science, Vol. 2295, Springer, 207-222.
https://doi.org/10.1007/978-981-96-7030-7_15

has been cited by the following article:

• TITLE: Filter before You Solve: A Deterministic-First/Learned-Second Architecture for AI-Driven Portfolio Management with Real-Money Training-Investment Calibration

  AUTHORS: George Melville, Dena Ghiassi, Scott Inthathirath, Julian Yeomans

  KEYWORDS: Explainable AI in Finance, AI-Driven Strategy, Machine Learning Insight AI in Business, Options Management, Filter-before-You-Solve, Deterministic-First/Learned-Second Architecture, Hallucination-Free, Interpretability, SimDec, Real-Money Training

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.19 No.8, August 26, 2026

  ABSTRACT: This study investigates a deterministic-first/learned-second AI/ML framework that is deployed in regulated retail brokerage accounts. A two-stage calibration system is employed that combines historical back-testing with live recalibration via continuous position snapshots. The framework includes a unique explainability layer that employs the global sensitivity analysis method, SimDec, to identify the most influential components. SimDec renders every admissible decision fully attributable to a joint state of the input space. Because the attribution is an input to the trigger rather than a commentary on it, no generative step exists in the decision path and hallucination is structurally unreachable and, therefore, absent by construction. An intraday options trading financial framework is illustrated through a live training-investment cycle on long-call positions using “real money”. The major contribution of this research is the overall architectural and methodological framework. The filter-before-you-solve approach enables contributions to be evaluated independently of specific implementations. Beyond financial applications, it is described how the complete architectural pattern can actually generalize to many AI/ML deployment contexts that require auditable deterministic gating prior to learned inference.