用社会模拟增强LLM在社科研究中的创意与实证能力
MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation
- 通过社会仿真构建多层级规范约束下的动态目标路径
- 生成论文质量提升6.81%，洞察力提高17.19%
- 适合需要深度洞见的社科领域自动化研究者
基于大语言模型（LLMs）的智能研究代理在自动论文写作中展现出巨大潜力。然而，现有系统主要依赖互联网和本地知识库进行文献检索与综述，导致社科研究缺乏深度洞察与创造性。为此，我们提出“记忆增强型社会模拟”（MASS）新范式，利用高度真实且面向研究的社会仿真，提升LLM生成研究的创造力与实证基础。MASS包含三个核心组件：多层级社会规范约束下的动态目标路径规划、支持多学科行为数据的代理记忆冷启动机制，以及受艾宾浩斯遗忘曲线启发的结构化遗忘机制。三者共同保障仿真真实性，为生成创新性学术论文提供坚实实证基础。实验表明，该方法在生成质量上相比基础LLM提升6.81%，在洞察力上较强基线提升17.19%。
原文摘要 · Abstract (English)
Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing systems rely heavily on literature retrieval and synthesis through internet and local knowledge bases, often resulting research in lacking insight and creativity in social science. To address this issue, we propose "Memory-Augmented Social Simulation (MASS)", an innovative paradigm that leverages highly realistic and research-oriented social simulations to enhance the creativity and empirical founding of LLMs-generated research. Specifically, MASS integrates three core components: dynamic goal-path planning with multi-level social norm restraint to guide the simulation, a multi-disciplinary behavior dataset for agent memory cold-start, and a structured forgetting mechanism inspired by the Ebbinghaus curve. Together, these ensure simulation authenticity and provide a robust empirical foundation for generating innovative scholarly papers. Experimental results demonstrate the effectiveness of our method, showing a 6.81\% improvement in generation overall quality over foundation LLMs and 17.19\% gain in Insight over strong baselines.
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