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LLM agents simulate A/B tests using data-driven personas

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for simulating A/B tests using large language model (LLM) agents. These agents are conditioned on data-driven personas derived from real user behavioral signals, moving beyond synthetic or rule-based approaches. The system frames A/B test simulation as a structured question task and has demonstrated directional accuracy of 0.75-0.90 on a benchmark of 40 A/B tests, offering a path toward faster and more cost-effective experiment pre-screening. AI

IMPACT This research could significantly reduce the time and cost associated with product development by enabling faster, data-driven experimentation.

RANK_REASON The cluster contains a research paper detailing a new methodology for simulating A/B tests using LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM agents simulate A/B tests using data-driven personas

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32 / 100
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The cluster contains a research paper detailing a new methodology for simulating A/B tests using LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, product
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyad Benomar, Weronika {\L}ajewska, Leonardo Perelli, Saab Mansour ·

    Data-Driven Persona-Conditioned Agents for A/B Test Simulation

    arXiv:2609.01038v1 Announce Type: new Abstract: A/B testing is the gold standard for evaluating product changes, but each experiment requires real user traffic, engineering effort, and weeks of measurement. We propose a simulation framework that predicts A/B test outcomes using L…

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