F²Agent: Modality-Aware Fusion of Specialist Agents for Financial Trading
Y. Liu, H. Jin, S. Cai, M. Fang, X. Xiao, B. C. Ooi · 2026
What this means for traders
How much each data source counts toward a verdict should be an explicit, inspectable number, not something buried inside a block of text.
Abstract
Most LLM trading systems merge market data, indicators, news and sentiment by concatenating them into a single prompt, which lets the textual signal dominate and loses cross-modal dependencies. F²Agent instead assigns one specialist encoder per modality and fuses them through learned modality-aware attention, regularised for prior diversity and for stability under single-modality perturbation. Across six assets it ranks first on annualized return against sixteen baselines, and its ablation shows the fusion layer, not the agents, carries the gain: replacing it with plain concatenation drops annualized return from 50.1% to 19.2% on AAPL.