# MultiHedge: Adaptive Coordination via Retrieval-Augmented Control

arXiv: https://arxiv.org/abs/2604.24905 · HTML: https://mlsanity.com/paper/2604.24905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Retrieval-augmented Large Language Models for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.05878/) (2025)
- [Sentiment trading with large language models](https://mlsanity.com/paper/2412.19245/) (2024)
- [DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization](https://mlsanity.com/paper/2509.12753/) (2025)

## Built on by (most cited first, 1 of 1)

- [More Claims, Less Evidence: Bounded Verification of AI-Generated Digital Knowledge Artifacts](https://mlsanity.com/paper/2610.05547/) (2026)

## Abstract (arXiv)

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.