8 citations · 10 across the 9 of their papers we have counts for
9 papers
X-Alpha: An AI Quant Researcher with Memory-Driven Self-Evolution
Fengyuan Liu, Yuchen Fu, Yuqi Wang +2
Human quantitative researchers mine alpha through an iterative process: they derive hypotheses from financial knowledge, implement and test them as factors, and use the resulting e…
Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
Haochen Luo, Yi Huang, Sichun Luo +5
Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-…
SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution
Sichun Luo, Yi Huang, Haochen Luo +7
Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through execu…
ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models
Sichun Luo, Yi Huang, Mukai Li +5
Large language models (LLMs) are increasingly deployed as conversational assistants in open-domain, multi-turn settings, where users often provide incomplete or ambiguous informati…
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution
Fengyuan Liu, Yi Huang, Sichun Luo +6
Discovering effective predictive signals, or "alphas," from financial data with high dimensionality and extremely low signal-to-noise ratio remains a difficult open problem. Despit…
Can an Individual Manipulate the Collective Decisions of Multi-Agents?
Fengyuan Liu, Rui Zhao, Shuo Chen +4
Individual Large Language Models (LLMs) have demonstrated significant capabilities across various domains, such as healthcare and law. Recent studies also show that coordinated mul…
Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
Fengyuan Liu, Haochen Luo, Yiming Li +2
Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model t…
An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models
Haochen Luo, Jindong Gu, Fengyuan Liu +1
Different from traditional task-specific vision models, recent large VLMs can readily adapt to different vision tasks by simply using different textual instructions, i.e., prompts.…
OpenFE: Automated Feature Generation with Expert-level Performance
Tianping Zhang, Zheyu Zhang, Zhiyuan Fan +5
The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning p…