arXiv:2604.05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.g., recommendation systems, dynamic portfolio adjustments, offer selection; all frequent problems in finance). While Large Language Models (LLMs) are increasingly applied to these settings, utilizing LLMs for reasoning at every decision step is computationally expensive, and uncertainty estimates are difficult to obtain. To address this, we introduce LLMP-UCB, a bandit algorithm that de
The proliferation of LLMs into decision-making systems necessitates mechanisms to manage their computational cost and provide uncertainty estimates, particularly for financial applications where efficiency and reliability are paramount.
This development addresses a critical bottleneck in deploying highly capable AI models in real-world, high-frequency decision environments, potentially accelerating AI adoption in finance and other sectors.
The ability to selectively apply LLMs or their representations in bandit algorithms means more efficient and reliable AI-driven decision-making, moving beyond brute-force LLM application.
- · AI algorithm developers
- · Quantitative finance firms
- · Recommendation system providers
- · Inefficient LLM integration approaches
- · Systems unprepared for adaptive AI deployment
More sophisticated and computationally efficient AI systems begin to replace simpler models in dynamic decision-making.
Financial institutions gain a competitive edge by deploying AI faster and more reliably in areas like portfolio management and offer selection.
The reduced cost and increased reliability of LLM integration could lead to their broader and deeper penetration across various industries currently constrained by computational overhead or lack of explainability.
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Read at arXiv cs.AI