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arXiv Computation and Language By Hanglong Lv, Dawei Zhu, Lei Li, Bowen Ye, Huaqiu Liu, Yifan Song, Bofei Gao, Weimin Xiong, Jinhao Dong, Chenhong He, Lingpeng Kong, Qi Liu, Tong Yang, Fuli Luo

PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems

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PersonaForge is a user‑simulation framework that generates realistic multi‑turn interactions between users and agentic systems, addressing the gap that most training data assumes single‑turn queries. It uses a four‑dimensional persona space, SOUL‑driven behavioral control calibrated to real‑user statistics, and Reverse Deep Construction from authentic seed queries to create a 6.3K‑record training set and a 138‑task benchmark called PersonaForge‑Bench across 20 professional domains. Experiments with Qwen3.5‑27B show that training with PersonaForge improves composite scores by 4.1%, especially in Task Completion (+6.0%) and Response Quality (+6.8%), while also reducing turns and tool calls, indicating more efficient interactions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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