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cs.LG2026
Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting
Junyi Ye, Ivy Gateri Wanjiku
Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Po…
q-fin.ST2026
Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
Junyi Ye, Gargi Vijay Borde
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should ent…
cs.MA2026
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
Jiayi Chen, Junyi Ye, Guiling Wang
Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retrievers, agents, tools, and orchestrators, t…