Multi-Agent LLM Framework Automates Retail Price Taxonomy
A recent study published on arXiv (2608.12674) presents a context-aware Multi-Agent Framework aimed at automating the development of "Lines and Ladders" pricing taxonomies for large-scale retail operations. This framework employs specialized LLM agents to pinpoint essential attributes, extract multi-modal values, and implement hierarchical grouping logic, effectively tackling the issue of price consistency across millions of products. Tested with real-world enterprise data and currently in production, the 3-Agent system boasts an F1-score of 0.83 for Lines, surpassing single-agent benchmarks by alleviating cognitive overload. In the Food & Consumables sector, it achieves over 90% precision and more than 75% recall. The paper underscores the impracticality of manual governance for global retailers and the detrimental effects of inconsistent pricing on customer perception and sales cannibalization.
Key facts
- arXiv paper 2608.12674 introduces a Multi-Agent Framework for retail price taxonomy.
- The framework automates 'Lines and Ladders' pricing structures.
- It uses specialized LLM agents for attribute identification and hierarchical grouping.
- Evaluated on real-world enterprise data and deployed in production.
- 3-Agent system achieves F1-score of 0.83 for Lines.
- Outperforms single-agent baselines by mitigating cognitive overload.
- Achieves >90% precision and >75% recall in Food & Consumables.
- Addresses infeasibility of manual governance for millions of items.
Entities
Institutions
- arXiv