arXiv cs.AI· Wenji Zhang, Xilong Cheng, Yunxiao Qin, Mai Tu, Tongye Zhu, Yuhao Zhou, JingBo Ji, Yuting Tan, Duoyin Li, Hongjiang Xiao, Yuan Zhang·· 3 天前AI 评分24
PsyMemBehav：面向社交媒体行为模拟的心理学启发与记忆锚定两级身份到行动推理
PsyMemBehav: Psychology-Informed and Memory-Grounded Two-Level Identity-to-Action Reasoning for Social-Media Behavior Simulation
AI 导读
PsyMemBehav 通过两级身份到行动推理，将心理学启发的用户画像与检索记忆映射到平台行为，并基于 Reddit、Bluesky、Twitter 上 5,862 名真实用户的 5.57M 条行为构建了 PsyMemBehav-bench。
正文 · 原文
Authors:Wenji Zhang, Xilong Cheng, Yunxiao Qin, Mai Tu, Tongye Zhu, Yuhao Zhou, JingBo Ji, Yuting Tan, Duoyin Li, Hongjiang Xiao, Yuan Zhang
Abstract:Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and language in sparse, noisy behavioral evidence. We introduce PsyMemBehav, which maps psychology-informed profiles and retrieved memories to target-first platform behavior through two-level identity-to-action reasoning. To support this approach with comprehensive behavioral traces, we construct a dataset PsyMemBehav-bench from 5.57M observed behaviors of 5,862 real users (approximately 950 per user) across Reddit, Bluesky, and Twitter. Each recorded engagement is modeled as target selection followed by Comment, Like, or Repost and optional response generation. PsyMemBehav combines 41-dimensional user descriptors, top-5 BM25 memories, reverse-CoT supervision, and rubric-guided DPO over complete reasoning-action trajectories. Experiments show that PsyMemBehav 27B ranks first in 11 of 12 reported individual-level metrics, improving average response quality over GPT-5.4 from 54.28 to 59.68 (9.95% relative), and obtains the lowest group-level MAE (0.081) and MSE (0.009) among simulated systems. These results support evidence-grounded user modeling for observed-thread continuation; the benchmark remains conditional on recorded engagement and does not model no-action decisions.
| Comments: | 20 pages, 7 figures, including appendices |
| Subjects: | Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2610.14570 [cs.AI] |
| (or arXiv:2610.14570v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14570 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xilong Cheng [view email]
[v1]
Sat, 10 Oct 2026 04:38:44 UTC (10,964 KB)
来源：arXiv cs.AI · arxiv.org