Optimizing Multi-Modal Financial Intelligence via Resource-Aware Distributed Scheduling for Large Language Model Enhanced Time Series Inference Pipelines - Vance, Alistair; Pennington, Matthew (Computational Intelligence Systems, 2026)
The modern financial landscape is increasingly defined by the synthesis of high-frequency numerical data and unstructured linguistic context. As Large Language Models (LLMs) evolve from general-purpose assistants to specialized reasoning engines, their integration into time series forecasting pipelines has become a primary objective for institutional financial intelligence. However, the computational intensity of transformer-based architectures introduces significant latency and resource contention within distributed systems, often rendering real-time inference unfeasible. This paper explores the architectural requirements and systemic optimizations necessary for multi-modal financial intelligence systems. We propose a resource-aware distributed scheduling framework designed specifically to manage the heterogeneous demands of concurrent time series analysis and LLM-driven semantic synthesis. By examining the structural trade-offs between inferential precision and execution throughput, the research highlights the necessity of hardware-aware orchestration in large-scale financial deployments. The discussion extends to the socio-technical implications of such systems, focusing on algorithmic governance, environmental sustainability, and the critical need for robustness in volatile market environments. Through a comprehensive system-level analysis, we demonstrate how optimized scheduling protocols can mitigate the bottleneck of cross-modal data fusion, ensuring that financial intelligence remains both semantically deep and temporally relevant. The paper concludes with an examination of the policy and ethical frameworks required to govern autonomous financial agents in a globalized, multi-modal economy.