AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
cs.AI, cs.CE, cs.MA
Submitted: 2026-09-24
Updated: 2026-09-24
Comments: 33 pages, 7 figures, 26 Tables. Preprint under review
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- AQuA: Recursively Self-Improving Quantitative Trading Research Agents
- QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
- Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO
- LoRA: Low-Rank Adaptation of Large Language Models
- AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment
- MARFT: Multi-Agent Reinforcement Fine-Tuning
- X-Alpha: An AI Quant Researcher with Memory-Driven Self-Evolution
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
- Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
- Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements
- Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery
- AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay
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