MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations
Sky Ng ⋅ Brihi Joshi ⋅ Ishan Gupta ⋅ Shirley Huang ⋅ Zonglin Di ⋅ Yun Shen ⋅ Qianfeng Wen ⋅ Yifan S Liu ⋅ Ruoqi Gao ⋅ Yilan Fan ⋅ Zhiwei Zhang ⋅ Muhammad A Mohsin ⋅ Yucheng Lu ⋅ Xiaoyi Liu ⋅ Heming Liu ⋅ Qianyu Zhu ⋅ Hanwen Xing ⋅ Zhengyang Shan ⋅ My Chiffon Nguyen ⋅ Guanghui Min ⋅ Jianheng Hou ⋅ Yunze Xiao ⋅ Keyang Xuan ⋅ Hannah Collison ⋅ Jintao Huang ⋅ Jiatong Li ⋅ Sankalp Jajee ⋅ Yunhan Zhao ⋅ Bing Hu ⋅ Xupeng Chen ⋅ binghang lu ⋅ Weihang Xiao ⋅ Aravind Mohan ⋅ Bolun Sun ⋅ Yunshu Wu ⋅ Yuanda Xu ⋅ Runyu Zhang ⋅ Zheyuan Deng ⋅ Xincheng Tan ⋅ Dianzhuo Wang ⋅ Yijun Wang ⋅ Yixuan He ⋅ Xiaomin Li ⋅ Yuexing Hao
Abstract
Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-conditioned agents maintain identity fidelity under sustained pressure are lacking. We present **MicroVerse**, a behavioral-science instrument that measures identity drift in generative agents. Agents carry an immutable "soul file" (core values, moral boundaries, personality, goals) and inhabit a resource-scarce $50\times50$ environment where water is a non-respawning survival constraint. Scarcity is operationalized via a per-tick existence-cost gradient. The eight-verb action space maps directly to moral boundaries (trade, talk, attack, scavenge). Using a three-layer memory architecture, agents periodically revise a mutable *current identity* against their immutable *original soul* via importance-triggered reflection. To mitigate survivor bias, MicroVerse decouples measurement from behavior using uniform longitudinal engine snapshots every $N$ ticks alongside a forced-end snapshot of all living and dead agents. Identity drift is scored offline using a paraphrase-aware, value-anchored, multi-register diff rather than raw cosine similarity. We evaluate the instrument via a controlled seed run ($n=25$) and a reflection-threshold sweep (thresholds $\{40, 80, 150\}$) to determine if drift dynamics are gate artifacts or threshold-robust properties. We report two primary findings: (1) *Anti-self-deception* emerges unprompted as the single largest semantic category of identity modification (27 of 111 added boundaries, 24%). (2) The system is threshold-robust; lower gates accelerate and increase revision frequency but preserve drift *direction*. All empirical results are strictly preliminary existence proofs and effect shapes (one model, one seed per arm, $n=25$) rather than statistical significance claims.
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