Empowering Real-Time Financial Decision Systems via Reinforcement Learning Driven Large Language Models and Distributed Temporal Pipelines - Carver, Paul (Computational Intelligence Systems, 2026)
The modern financial ecosystem is characterized by an unprecedented volume of high-velocity data and the increasing necessity for context-aware, autonomous decision-making. Traditional quantitative models, while effective at identifying statistical regularities in numerical time series, often lack the semantic depth required to navigate complex market narratives and geopolitical shifts. This paper proposes a novel system architecture that empowers real-time financial decision systems by integrating reinforcement learning-driven large language models with high-throughput distributed temporal pipelines. We explore the structural requirements for a unified infrastructure that can synthesize high-frequency market signals with the qualitative reasoning capabilities of transformer-based architectures. Central to our discussion is the design of a reinforcement learning framework that optimizes large language model outputs for specific financial objectives, such as risk-adjusted returns and market stability, rather than linguistic fluency alone. We provide an extensive analysis of system-level trade-offs, emphasizing the tension between inferential depth and execution latency in sub-millisecond trading environments. Furthermore, the research addresses critical socio-technical dimensions, including the governance of autonomous financial agents, the environmental sustainability of massive-scale distributed inference, and the ethical implications of algorithmic fairness in capital allocation. By aligning the precision of reinforcement learning with the interpretive power of large language models, this framework offers a robust blueprint for the next generation of financial infrastructures, ensuring that autonomous decision-making is both statistically rigorous and contextually grounded in a volatile global economy.