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AQuA is a research system with two independent language-model-driven agents that recursively self-improve in quantitative trading research, achieving strong information coefficients on crypto and US equities while using sealed sandboxes to prevent data leakage.
This paper introduces DKCD, a framework that enhances causal discovery from unstructured data in high-expertise domains by integrating domain knowledge graphs with LLM-based reasoning to identify latent causal factors and improve annotation accuracy.