让小模型学会感知情绪，看它如何影响决策。
On Emotion-Sensitive Decision Making of Small Language Model Agents
- 用真实情绪文本诱导模型情绪状态，实现可控干预。
- 情绪变化会系统性改变策略选择，但结果常不稳定。
- 适合研究模型行为鲁棒性与人机交互的学者。
小型语言模型（SLM）越来越多地作为交互式决策代理使用，但现有评估大多忽略情绪对行为的影响。本文通过将表示级情绪诱导与结构化博弈论评估结合，研究情绪敏感型决策。利用来自经过众包验证的真实情绪诱发文本的激活控制技术，实现超越提示工程的可控制、可迁移的情绪干预。构建了一个基准测试，围绕典型的决策模板展开，涵盖合作与竞争激励，以及完全与不完全信息场景。这些模板基于《Diplomacy》《StarCraft II》中的战略情境及多种现实人格角色实例化。在多个模型家族、不同架构和模态下进行实验，结果显示情绪扰动会系统性影响战略选择，但行为往往不稳定，且不完全符合人类预期。最后，提出一种提升对情绪驱动扰动鲁棒性的方法。
原文摘要 · Abstract (English)
Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a causal factor influencing behavior. We study emotion-sensitive decision making by combining representation-level emotion induction with a structured game-theoretic evaluation. Emotional states are induced using activation steering derived from crowd-validated, real-world emotion-eliciting texts, enabling controlled and transferable interventions beyond prompt-based methods. We introduce a benchmark built around canonical decision templates that span cooperative and competitive incentives under both complete and incomplete information. These templates are instantiated using strategic scenarios from \textsc{Diplomacy}, \textsc{StarCraft II}, and diverse real-world personas. Experiments across multiple model families in various architecture and modalities, show that emotional perturbations systematically affect strategic choices, but the resulting behaviors are often unstable and not fully aligned with human expectations. Finally, we outline an approach to improve robustness to emotion-driven perturbations.
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