Scaling High-Frequency Financial Intelligence using Adaptive Resource Scheduling for Multi-Modal Large Language Model Enhanced Inference
DOI:
https://doi.org/10.66280/cis.v1i1.235Keywords:
Financial Intelligence, Adaptive Resource Scheduling, Multi-Modal Large Language Models, High-Frequency Inference, Distributed Systems, Socio-Technical Infrastructure, Algorithmic GovernanceAbstract
The modern financial ecosystem is increasingly defined by the synthesis of high-frequency numerical data and unstructured linguistic context. As Multi-Modal Large Language Models (MM-LLMs) evolve from general-purpose assistants to specialized reasoning engines, their integration into high-frequency financial intelligence pipelines has become a primary objective for institutional systems. However, the computational intensity of transformer-based architectures introduces significant latency and resource contention within distributed environments, often rendering real-time inference unfeasible for latency-sensitive applications. This paper explores the architectural requirements and systemic optimizations necessary for scaling financial intelligence through adaptive resource scheduling. We propose a framework that manages the heterogeneous demands of concurrent time series analysis and semantic synthesis by dynamically reallocating compute resources based on market volatility and model complexity. 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.
References
1. Abadi, Martin, Chu, Andy, Goodfellow, Ian, McMahan, Brendan, Mironov, Ilya, Talwar, Kunal, & Zhang, Li. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
2. Acemoglu, Daron, & Restrepo, Pascual. (2019). Automation and new tasks: How technology displaces and creates labor. Journal of Economic Perspectives, 33(2), 3-30.
3. Bommasani, Rishi, et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
4. Brown, Tom, Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, ... & Amodei, Dario. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
5. Cartea, Alvaro, Jaimungal, Sebastian, & Penalva, Jose. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.
6. Chen, Lawrence, & Zheng, Zeyu. (2023). LLM-augmented financial analysis: Challenges and opportunities. Journal of Financial Data Science, 5(4), 12-28.
7. Dean, Jeffrey, & Ghemawat, Sanjay. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
8. Dwork, Cynthia. (2008). Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation, 1-19.
9. Engle, Robert. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
10. Ghoshal, Biswajit, & Tucker, Allan. (2022). Scalable inference for deep learning in finance. Quantitative Finance, 22(10), 1845-1860.
11. Goodfellow, Ian, Bengio, Yoshua, & Courville, Aaron. (2016). Deep Learning. MIT Press.
12. Goyal, Naman, et al. (2023). High-throughput inference for large language models: A systems perspective. ACM SIGOPS Operating Systems Review, 57(1), 45-56.
13. Hendershott, Terrence, Jones, Charles, & Menkveld, Albert. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1-33.
14. Kaplan, Jared, McCandlish, Sam, Henighan, Tom, Brown, Tom, Chess, Benjamin, Child, Rewon, ... & Amodei, Dario. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
15. Kirilenko, Andrei, Kyle, Albert, Samadi, Mehrdad, & Tuzun, Tugkan. (2017). The Flash Crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3), 967-998.
16. Lo, Andrew. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
17. Lopez de Prado, Marcos. (2018). Advances in Financial Machine Learning. Wiley.
18. Narayanan, Deepak, Phanishayee, Amar, Shi, Kaiyu, Chen, Xie, & Zaharia, Matei. (2019). PipeDream: Generalized pipeline parallelism for DNN training. Proceedings of the 27th ACM Symposium on Operating Systems Principles.
19. O’Neil, Cathy. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
20. Liu, T. (2026). Leakage-Safe Benchmark Design for Market-Stress Early Warning: An Economically Credible Evaluation.
21. Pasquale, Frank. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
22. Rajbhandari, Samyam, Rasley, Jeff, Ruwase, Olatunji, & He, Yuxiong. (2020). ZeRO: Memory optimizations toward training trillion parameter models. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.
23. Shalf, John. (2020). The future of computing beyond Moore’s Law. Philosophical Transactions of the Royal Society A, 378(2166).
24. Stoica, Ion, et al. (2017). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation.
25. Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan, ... & Polosukhin, Illia. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
26. Wu, Shijie, et al. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
27. Zaharia, Matei, et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX Symposium on Networked Systems Design and Implementation.
28. Zhou, Yanqi, et al. (2022). Mixture-of-experts with exponential selection. arXiv preprint arXiv:2202.08906.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
