视觉提示对视觉语言模型合作行为的影响
The Effects of Visual Priming on Cooperative Behavior in Vision-Language Models
AI总结：
本文研究了视觉提示如何影响视觉语言模型在囚徒困境中的合作行为，通过图像内容和颜色奖励矩阵实验，发现图像内容和颜色提示对模型决策模式有影响，不同模型的敏感性和缓解效果各异。
摘要：
随着视觉语言模型（VLMs）在决策系统中的集成日益增加，了解视觉输入如何影响其行为变得至关重要。本文通过重复囚徒困境（IPD）测试场景，探讨了视觉提示对VLMs合作行为的影响。我们检查了暴露于描绘行为概念（善良/助人 vs. 激进/自私）的图像和颜色编码奖励矩阵是否改变VLM决策模式。在多个最先进的VLM上进行了实验，并进一步探索缓解策略，包括提示修改、思维链（CoT）推理和视觉令牌减少。结果表明，VLM行为受图像内容和颜色提示的影响，不同模型的敏感性和缓解效果各异。这些发现不仅强调了在视觉丰富和安全关键环境中部署VLMs时稳健评估框架的重要性，还突显了模型架构和训练差异可能导致不同的行为响应，这值得进一步研究。
英文摘要：
As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence their behavior. This paper investigates the effects of visual priming on VLMs' cooperative behavior using the Iterated Prisoner's Dilemma (IPD) as a test scenario. We examine whether exposure to images depicting behavioral concepts (kindness/helpfulness vs. aggressiveness/selfishness) and color-coded reward matrices alters VLM decision patterns. Experiments were conducted across multiple state-of-the-art VLMs. We further explore mitigation strategies including prompt modifications, Chain of Thought (CoT) reasoning, and visual token reduction. Results show that VLM behavior can be influenced by both image content and color cues, with varying susceptibility and mitigation effectiveness across models. These findings not only underscore the importance of robust evaluation frameworks for VLM deployment in visually rich and safety-critical environments, but also highlight how architectural and training differences among models may lead to distinct behavioral responses-an area worthy of further investigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
