Data-Driven Persona-Conditioned Agents for A/B Test Simulation
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 LLM-powered agents conditioned on data-driven personas grounded in real user behavioral signals. Unlike prior work that relies on synthetic or rule-based personas, our agents are constructed from anonymized behavioral data-activity patterns, engagement signals, and inferred demographics-enabling more faithful population modeling. We frame A/B test simulation as a str
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 60%AI can’t simulate human preferences - new study tests LLMs against thousands of real users →
- LinkedLinked via arxiv author · 85%Ziyad Benomar →
“Data-Driven Persona-Conditioned Agents for A/B Test Simulation”
- LinkedLinked via arxiv author · 85%Weronika Łajewska →
“Data-Driven Persona-Conditioned Agents for A/B Test Simulation”
- LinkedLinked via arxiv author · 85%Leonardo Perelli →
“Data-Driven Persona-Conditioned Agents for A/B Test Simulation”
- LinkedLinked via arxiv author · 85%Saab Mansour →
“Data-Driven Persona-Conditioned Agents for A/B Test Simulation”
- PossiblePossibly related (embedding) · 51%Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions - Nature →
- FuzzyOverlapping authors or contributors · 62%strands-agents/harness-sdk →
“Shared author/contributor keys: agents”