# LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading

arXiv: https://arxiv.org/abs/2501.09636 · HTML: https://mlsanity.com/paper/2501.09636/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts.
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Placed in

- Mixture-of-experts / [MoE for time series and graphs](https://mlsanity.com/moe/moe-for-time-series-and-graphs/)

## Builds on (most cited first, 3 of 3)

- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Automate Strategy Finding with LLM in Quant Investment](https://mlsanity.com/paper/2409.06289/) (2024)

## Built on by (most cited first, 7 of 7)

- [BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts](https://mlsanity.com/paper/2504.18598/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agents](https://mlsanity.com/paper/2603.22567/) (2026)
- [Routing-Free Mixture-of-Experts](https://mlsanity.com/paper/2604.00801/) (2026)
- [iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis](https://mlsanity.com/paper/2607.08778/) (2026)
- [TradingMoE: Routing the Right Experts in Evolving Markets](https://mlsanity.com/paper/2608.11785/) (2026)
- [Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching](https://mlsanity.com/paper/2608.28252/) (2026)

## Abstract (arXiv)

Recent advances in deep learning and large language models (LLMs) have facilitated the deployment of the mixture-of-experts (MoE) mechanism in the stock investment domain. While these models have demonstrated promising trading performance, they are often unimodal, neglecting the wealth of information available in other modalities, such as textual data. Moreover, the traditional neural network-based router selection mechanism fails to consider contextual and real-world nuances, resulting in suboptimal expert selection. To address these limitations, we propose LLMoE, a novel framework that employs LLMs as the router within the MoE architecture. Specifically, we replace the conventional neural network-based router with LLMs, leveraging their extensive world knowledge and reasoning capabilities to select experts based on historical price data and stock news. This approach provides a more effective and interpretable selection mechanism. Our experiments on multimodal real-world stock datasets demonstrate that LLMoE outperforms state-of-the-art MoE models and other deep neural network approaches. Additionally, the flexible architecture of LLMoE allows for easy adaptation to various downstream tasks.