Reproduce “Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Wonduk Seo, Taesub Shin, Hyunjin An, Dokyun Kim, Seunghyun Lee
The Q2K framework from Enhans AI Research introduces a multi-agent system for e-commerce Stock Keeping Unit (SKU) mapping that generates inspectable facts to determine product equivalence. It achieved 95.62% accuracy on a real-world dataset, outperforming LLM baselines, and reduced web search costs by reusing reasoning traces in approximately 22% of cases.
Key insights
- Decomposing complex product mapping into specific, attribute-focused questions significantly enhances clarity and enables targeted external knowledge retrieval, improving overall accuracy.
- Leveraging general-purpose LLMs as flexible reasoning modules, augmented by dynamic web search and a self-updating knowledge base, offers a more adaptable and cost-efficient solution than continuous fine-tuning for rapidly evolving e-commerce catalogs.
- The ability to identify and reuse previously validated reasoning traces through a deduplication mechanism substantially reduces operational costs and latency by avoiding redundant web searches for recurring product comparison patterns.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.