arXiv AI
EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research
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arXiv AI
Sep 25
AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
AlphaDiverse is a framework that enhances large language model–based multi‑agent systems for alpha factor mining by addressing cost, availability, and confidentiality constraints. It generates diverse research paths through varied environments and post‑training local agents, then fine‑tunes these agents with supervised learning and optimizes them jointly using a GRPO method that balances predictive quality and diversity. The approach limits research feedback to inner‑period data and evaluates a frozen model on outer‑period data to avoid test‑set tuning, demonstrating competitive prediction and broader exploration across four Chinese stock universes.
arXiv AI
Sep 16
FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
arXiv:2603.16365v3 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement th...
arXiv AI
Jun 3
AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
arXiv:2508. 13174v2 Announce Type: replace Abstract: Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment.
arXiv Computation and Language
Oct 16
X-Alpha: An AI Quant Researcher with Memory-Driven Self-Evolution
X-Alpha is an AI Quant Researcher that uses persistent research memory and continuous hypothesis-to-code discovery to overcome key bottlenecks in automated alpha mining. It converts financial reports into actionable research paths via Report-to-Memory Abstraction, directs exploration with memory-driven routing, and refines hypotheses through executable factors and empirical validation. Experiments on five equity indices, especially CSI300, show that X-Alpha improves factor quality, signal diversity, and consistently outperforms baselines across markets and horizons.
arXiv AI
Oct 26
AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents
AlphaMemo is a self‑evolving alpha‑mining agent that uses Structured Search‑Process Memory to improve factor discovery. Instead of storing only final factors or full search trajectories, it records reusable evidence about which edit motifs succeed or fail in specific parent‑factor contexts, extracted from Abstract Syntax Tree differences. The system incorporates confidence‑gated residual memory, a search‑ledger prior, and asymmetric veto control to suppress high‑confidence failure patterns, leading to better out‑of‑sample performance and fixed‑budget discovery efficiency on CSI 500 and S&P 500 datasets.
arXiv AI
Jul 14
FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.