[2603.23231] PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments

Computer Science > Artificial Intelligence arXiv:2603.23231 (cs) [Submitted on 24 Mar 2026] Title:PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments Authors:Shuochen Liu, Junyi Zhu, Long Shu, Junda Lin, Yuhao Chen, Haotian Zhang, Chao Zhang, Derong Xu, Jia Li, Bo Tang, Zhiyu Li, Feiyu Xiong, Enhong Chen, Tong Xu View a PDF of the paper titled PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments, by Shuochen Liu and 13 other authors View PDF HTML (experimental) Abstract:Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. However, prior evaluations typically interleave preference-related dialogues with irrelevant conversations, reducing the task to needle-in-a-haystack retrieval while ignoring relationships between events that drive the evolution of user preferences. Such settings overlook a fundamental characteristic of real-world personalization: preferences emerge gradually and accumulate across interactions within noisy contexts. To bridge this gap, we introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall. Additionally, we incorporate (1) text variability and (2) linguistic alignment to simulate erratic user inputs and individual idiolects in real-world data. PERMA consists of temporally ordered interaction events spanning multiple sess...