Decision-making under changing conditions remains a fundamental challenge in many real-world systems. Existing approaches often fail to generalize across shifting regimes and exhibit unstable behavior under uncertainty. This raises the research question: can retrieval-augmented LLM coordination improve the robustness of modular decision pipelines? We propose MultiHedge, a hybrid architecture where an LLM produces structured allocation decisions conditioned on retrieved historical precedents, and execution is grounded in canonical option strategies. In a controlled evaluation using U.S. equities, we compare MultiHedge to rule-based and learning-based baselines. The key result is that memory-augmented retrieval confers greater robustness and stability than increasing model scale alone. Our paper contributes a controlled computational study showing that memory and architectural design play a central role in robustness in modular decision systems.
@article{arxiv.2604.24905,
title = {MultiHedge: Adaptive Coordination via Retrieval-Augmented Control},
author = {Feliks Bańka and Jarosław A. Chudziak},
journal= {arXiv preprint arXiv:2604.24905},
year = {2026}
}
Comments
8 pages, 2 figures. Accepted to the 26th International Conference on Computational Science (ICCS 2026), to appear in Springer LNCS proceedings