Mitigating Identity Essentialism in LLM Agents with Longitudinal Life Trajectories

2026-08-20

AI Summary

研究通过提出LifeMem框架，结合生活事件记忆和参数记忆，解决大型语言模型中静态代理导致的身份本质主义问题，提高模拟人类多样性的能力。

Abstract

Large language models (LLMs) offer a scalable approach to social simulation, but their credibility depends on how agents are constructed. Existing methods can partially reproduce population-level patterns, yet often fail to capture human-like diversity. Our analysis shows that static-profile agents exhibit stronger demographic separation and within-group compression than humans, a pattern consistent with identity essentialism: demographic labels can encourage models to treat group-average tendencies as individual traits, homogenizing responses within groups. We argue that this limitation arises from two related factors: sparse, static agent representations and the limited ability of prompt-only memory to persistently integrate experience. Inspired by complementary memory systems, we propose LifeMem, a longitudinal memory framework that combines structured life-event retrieval with agent-specific parametric memory for experience integration. Experiments on Add Health and Understanding Society with three LLMs show that LifeMem improves alignment with human data in terms of response distributions, overall and within-group diversity, and patterns of within-person response change across life stages. These findings highlight the value of longitudinal life-event memory for constructing more faithful and dynamically evolving social agents.

Problem

• identity essentialism
• large language models
• social simulation
• agent diversity
• longitudinal memory

Innovation

• Longitudinal Memory
• LifeMem
• Experience Integration
• Social Agents