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 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.