OptAgent: Optimizing Query Rewriting for E-commerce via Multi-Agent Simulation

• URL: http://arxiv.org/abs/2510.03771v1
• Date: Sat, 04 Oct 2025 10:41:09 GMT
• Title: OptAgent: Optimizing Query Rewriting for E-commerce via Multi-Agent Simulation
• Authors: Divij Handa, David Blincoe, Orson Adams, Yinlin Fu,
• Abstract summary: OptAgent is a novel framework that combines multi-agent simulations with genetic algorithms to verify and optimize queries for e-commerce queries.<n>We evaluate OptAgent on a dataset of 1000 real-world e-commerce queries in five different categories.
• Score: 1.3722079106827219
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying capable and user-aligned LLM-based systems necessitates reliable evaluation. While LLMs excel in verifiable tasks like coding and mathematics, where gold-standard solutions are available, adoption remains challenging for subjective tasks that lack a single correct answer. E-commerce Query Rewriting (QR) is one such problem where determining whether a rewritten query properly captures the user intent is extremely difficult to figure out algorithmically. In this work, we introduce OptAgent, a novel framework that combines multi-agent simulations with genetic algorithms to verify and optimize queries for QR. Instead of relying on a static reward model or a single LLM judge, our approach uses multiple LLM-based agents, each acting as a simulated shopping customer, as a dynamic reward signal. The average of these agent-derived scores serves as an effective fitness function for an evolutionary algorithm that iteratively refines the user's initial query. We evaluate OptAgent on a dataset of 1000 real-world e-commerce queries in five different categories, and we observe an average improvement of 21.98% over the original user query and 3.36% over a Best-of-N LLM rewriting baseline.

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