arXiv：cs.CL· Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell, Chris Callison-Burch, Lyle Ungar, Dan Roth, Iordanis Fostiropoulos, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu·· 4 小时前AI 评分41
PersonaMem-v3：面向全平台个人智能的基准，覆盖整体用户理解、推荐与智能体任务
PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
AI 导读
PersonaMem-v3 是一个面向全平台个人智能的基准与评测框架，基于超过 100 万条匿名真实互动历史构建按时间索引的用户数字世界，覆盖社交媒体、聊天机器人、日历和 AI 伴侣场景，并体现偏好随时间演变。
正文
Authors:Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell, Chris Callison-Burch, Lyle Ungar, Dan Roth, Iordanis Fostiropoulos, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu
Abstract:Personal intelligence is becoming a central frontier for user-facing AI agents. To be helpful in everyday life, agents must understand users across the digital contexts where their preferences, intents, habits, social relationships, and needs unfold over time. Today's systems can personalize within individual apps or tasks, but personal intelligence as a whole remains under-measured: how agents build cross-context user understanding, support steerable recommendation systems, act proactively across platforms, and avoid over-personalization. We introduce PersonaMem-v3, a real-world-grounded benchmark and evaluation harness for omni-platform personal intelligence. PersonaMem-v3 is seeded from more than one million anonymized real-world engagement histories, most of which are implicit signals, and uses them to construct time-indexed user digital worlds across social media, chatbot, calendar, and AI-companion with preference evolvement over time. The benchmark brings personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning into one framework, anchored in psychology, social-linguistics, and user-behavior theories. It evaluates whether AI agents can infer holistic user understanding from cross-platform evidence, personalize responses, rerank recommendations on social media, follow user steering through natural language, and hold back when personalization would be inappropriate, repetitive, outdated, or unnecessary. PersonaMem-v3 points toward LLM-powered personal intelligent agents that work with existing scalable recommendation infrastructure while making personalization more interactive, agentic, and aligned with how real users experience their digital lives.
| Subjects: | Computers and Society (cs.CY); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.21381 [cs.CY] |
| (or arXiv:2608.21381v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2608.21381 arXiv-issued DOI via DataCite |
Submission history
From: Bowen Jiang [view email]
[v1]
Thu, 16 Jul 2026 19:33:11 UTC (14,522 KB)
[v2]
Wed, 7 Oct 2026 05:26:50 UTC (14,522 KB)
来源：arXiv：cs.CL · arxiv.org