MultiHedge: Adaptive Coordination via Retrieval-Augmented Control

• URL: http://arxiv.org/abs/2604.24905v1
• Date: Mon, 27 Apr 2026 18:38:22 GMT
• Title: MultiHedge: Adaptive Coordination via Retrieval-Augmented Control
• Abstract summary: MultiHedge is a hybrid architecture where an LLM produces structured allocation decisions conditioned on retrieved historical precedents.<n>Our paper contributes a controlled computational study showing that memory and architectural design play a central role in robustness in modular decision systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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