QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Haoxue Wang, Keli Wen, Yuante Li, Qiancheng Qu, Xiangxu Mu, Xinjie Shen, Jiaqi Gao, Chenyang Chang, Chuhan Xie, San Yu Cheung, Zhuoyuan Hu, Xinyu Wang, Sirui Bi, Bi'an Du
Published: 2025/9/25
Abstract
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows. To tackle this, We present QuantMind, an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. QuantMind adopts a two-stage architecture: (i) a knowledge extraction stage that transforms heterogeneous documents into structured knowledge through multi-modal parsing of text, tables, and formulas, adaptive summarization for scalability, and domain-specific tagging for fine-grained indexing; and (ii) an intelligent retrieval stage that integrates semantic search with flexible strategies, multi-hop reasoning across sources, and knowledge-aware generation for auditable outputs. A controlled user study demonstrates that QuantMind improves both factual accuracy and user experience compared to unaided reading and generic AI assistance, underscoring the value of structured, domain-specific context engineering for finance.