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RADAR is introduced, a retrieval-augmented diffusion framework that learns market representations by conditioning on similar historical regimes that achieves state-of-the-art performance on key risk-adjusted metrics while producing economically meaningful signals on asset returns and correlations.
The proposed Multi-Agent Fundamental Analysis with Symbolic Adaptive learning (MUFASA), a hierarchical multi-agent framework for symbolic discovery in finance, achieves state-of-the-art performance on the valuation task compared to classical finance methods, financial large language models, and SR approaches.
SPACE is proposed, a conformal wrapper for sample-generating multivariate forecasters that consistently brings realized joint and rolling coverage closer to the nominal target, achieving superior coverage-efficiency tradeoffs relative to competing wrappers.
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