arXiv 论文提出 LLM 社会智能体的上下文增强与选择性鲁棒性框架
A Framework for Context Enrichment and Selective Robustness in LLM Social Agents
做 LLM 社会仿真的人看看，这篇给了套可复用的上下文增强规则和验证标准，还实测了两个模型的表现差异。
arXiv 论文针对 LLM 智能体在语义不变的提示词变体下行为大幅改变的问题，提出受控上下文增强框架，将上下文分为中性填充、通用社会背景、描述性决策锚定和规范性决策锚定四类。论文同时引入“选择性鲁棒性”作为验证标准，要求智能体对语义不变的提示变化不敏感，对社会世界中的决策相关变化做出方向性响应。研究在类囚徒困境博弈中演示该框架，测试了两个模型，发现不同类型的上下文增强产生不同行为模式，且两个模型之间的表现存在差异。
A Framework for Context Enrichment and Selective Robustness in LLM Social Agents
Researchers use large language models as agents in social simulations, where agents are prompted with observations and background information intended to shape their actions. Yet LLM agents have been shown to substantially change their behavior under meaning-preserving prompt variations, creating a validation problem for social simulation. One plausible hypothesis is that this brittleness reflects a lack of grounding and can be reduced by enriching the context provided to the agents. Whether richer context improves robustness remains unclear, in part because context richness and robustness are rarely defined in ways that support controlled comparisons. We address the first need with controlled context enrichment, a set of prompt substitution rules that systematically enriches distinct types of context: neutral padding, generic social background, descriptive decision grounding, and normative decision grounding. We address the second by introducing selective robustness as a validation criterion: insensitivity to meaning-preserving prompt variation combined with a directional response to decision-relevant changes in the social world. We demonstrate the framework in a Prisoner's Dilemma-like game, showing how it can be used to identify which forms of context enrichment produce more desirable and robust agent behavior. We find that these forms of enrichment produce distinct behavioral patterns that vary between the two models tested.