March 16 - 17, 2027
| Javits Center, New York
Decode the Market. Build the Future.Capture the Alpha.
Haoxue Wang
Quantitative Analyst Millennium Capital PartnersDAY ONE I Tuesday March 31
4:35 PM PRESENTATION: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Large language models are rapidly becoming part of everyday workflows for researchers, engineers, and investors. However, as these systems move from experimentation into production environments, a new bottleneck has emerged: the challenge is no longer simply writing better prompts, but managing context effectively.
Modern AI developer tools such as Claude Code and other coding agents demonstrate this shift clearly. These systems can read codebases, modify files, execute tasks, and iterate autonomously. Yet their performance depends heavily on how information is structured and supplied to the model. Poorly organized context often leads to brittle reasoning, hallucinated outputs, or inconsistent results.
This talk introduces QuantMind, a context-engineering based knowledge framework designed to address these challenges. The framework is based on our research paper, “QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance”.
QuantMind adopts a two-stage architecture. The first stage focuses on knowledge extraction, transforming heterogeneous financial documents into structured knowledge through multi-modal parsing of text, tables, and formulas, combined with adaptive summarization and domain-specific tagging for fine-grained indexing. The second stage focuses on intelligent retrieval, integrating semantic search with flexible retrieval strategies, multi-hop reasoning across sources, and knowledge-aware generation to produce auditable outputs grounded in explicit evidence.
Beyond finance, the talk will also discuss the broader shift from prompt engineering toward context engineering in modern AI systems. Tools such as Claude Code highlight how the performance of agentic systems increasingly depends on how context—documents, memory, and retrieval pipelines—is structured and managed. The QuantMind framework illustrates how domain-specific context engineering can significantly improve both factual accuracy and user experience in complex knowledge-intensive tasks.
A controlled user study demonstrates that this approach improves both accuracy and research efficiency compared to unaided document reading and generic AI assistance. The talk concludes with practical lessons for building reliable AI research systems in quantitative finance and other knowledge-heavy domains.
Check out the incredible speaker line-up to see who will be joining Haoxue.
Download The Latest Agenda
