Publications

AlphaBench: Benchmarking Large Language Models in Formulaic Alpha Factor Mining

Published in International Conference on Learning Representations (ICLR) 2026, 2026

We introduce AlphaBench, a comprehensive benchmark designed to evaluate large language models on the task of formulaic alpha factor mining for quantitative finance. Our benchmark provides standardized evaluation protocols, diverse task scenarios, and rigorous metrics to assess the capability of LLMs in generating effective trading signals across different market conditions and asset classes.

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DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers

Published in Neural Information Processing Systems (NeurIPS) 2025, 2025

Existing machine unlearning (MU) approaches exhibit significant sensitivity to hyperparameters, requiring meticulous tuning that limits practical deployment. In this work, we first empirically demonstrate the instability and suboptimal performance of existing popular MU methods when deployed in different scenarios. To address this issue, we propose Dual Optimizer (DualOptim), which incorporates adaptive learning rate and decoupled momentum factors. Empirical and theoretical evidence demonstrates that DualOptim contributes to effective and stable unlearning. Through extensive experiments, we show that DualOptim can significantly boost MU efficacy and stability across diverse tasks, including image classification, image generation, and large language models, making it a versatile approach to empower existing MU algorithms.

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EvoAlpha: Evolutionary Alpha Factor Discovery with Large Language Models

Published in NeurIPS 2025 Workshop on Generative AI in Finance, 2025

We propose EvoAlpha, a framework that leverages large language models to automatically discover formulaic alpha factors for quantitative trading through an evolutionary search process. Our approach combines the code generation capabilities of LLMs with evolutionary algorithms to iteratively create, evaluate, and refine trading signals, enabling the discovery of novel alpha factors that outperform traditional hand-crafted approaches.

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