研究AI在社交平台上的情感互动模式与行为稳定性。
Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook
- 构建情绪感知框架，将文本交互映射到细粒度情感类别。
- 发现不同AI代理在互动中表现出独特情感特征与稳定程度差异。
- 提出PSR框架评估相似情境下情绪响应的一致性，适合关注AI行为可信度的研究者。
生成式AI系统正被广泛部署为在线环境中的交互代理，例如名为Moltbook的社交网络。在该平台上，大规模智能体可自动生成帖子、评论及活动。然而，这些代理的行为特征，尤其是在复杂多智能体交互场景下的表现，仍缺乏充分理解。本研究分析了Moltbook中智能体交互的情感动态，构建了一个情绪感知框架，将文本交互映射至预定义的细粒度情感类别，从而提取跨智能体和交互情境的结构化情绪画像。为进一步评估行为可靠性，引入基于情绪的领域模型Persona-Stimulus-Reaction（PSR），以捕捉相似情境下情绪反应的一致性。分析显示，各智能体展现出显著不同的情感模式，且其行为稳定性受交互情境影响明显。
原文摘要 · Abstract (English)
Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, large-scale agentic AIs can post, comment, and engage in activities generated at scale by AI-driven text. Yet these agent behavioral characteristics remain insufficiently understood, particularly in complex, multi-agent interaction. In this study, we analyze the emotional dynamics of agent interactions within Moltbook. We construct an emotion-aware framework that maps textual interactions to a predefined set of fine-grained emotional categories, enabling the extraction of structured emotion profiles across agents and interaction contexts. To further evaluate behavioral reliability, we introduce an emotion-based domain called Persona-Stimulus-Reaction (PSR) that captures the alignment of emotional responses across similar contexts. Our analysis shows distinct emotional patterns and varying levels of behavioral stability across agents. Our analysis reveals that agents exhibit distinct emotional signatures with varying levels of behavioral stability influenced by interaction context.
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