Language models are increasingly used for social robot navigation, yet existing benchmarks largely overlook principled prompt design for socially compliant behavior. This limitation is particularly relevant in practice, as many systems rely on small vision language models (VLMs) for efficiency. Compared to large language models, small VLMs exhibit weaker decision-making capabilities, making effective prompt design critical for accurate navigation. Inspired by cognitive theories of human learning and motivation, we study prompt design along two dimensions: system guidance (action-focused, reasoning-oriented, and perception-reasoning prompts) and motivational framing, where models compete against humans, other AI systems, or their past selves. Experiments on two socially compliant navigation datasets reveal three key findings. First, for non-finetuned GPT-4o, competition against humans achieves the best performance, while competition against other AI systems performs worst. For finetuned models, competition against the model's past self yields the strongest results, followed by competition against humans, with performance further influenced by coupling effects among prompt design, model choice, and dataset characteristics. Second, inappropriate system prompt design can significantly degrade performance, even compared to direct finetuning. Third, while direct finetuning substantially improves semantic-level metrics such as perception, prediction, and reasoning, it yields limited gains in action accuracy. In contrast, our system prompts produce a disproportionately larger improvement in action accuracy, indicating that the proposed prompt design primarily acts as a decision-level constraint rather than a representational enhancement.

翻译：语言模型在社会机器人导航中的应用日益广泛，然而现有基准测试大多忽视了面向社会合规行为的系统性提示设计。这一局限在实践中尤为突出，因为许多系统出于效率考虑依赖小型视觉语言模型。与大型语言模型相比，小型视觉语言模型的决策能力较弱，因此有效的提示设计对于实现精确导航至关重要。受人类学习与动机认知理论的启发，我们从两个维度研究提示设计：系统引导（动作聚焦型、推理导向型和感知-推理型提示）与动机框架（模型与人类、其他AI系统或其自身历史表现进行竞争）。在两个社会合规导航数据集上的实验揭示了三个关键发现。首先，对于未经微调的GPT-4o模型，与人类竞争的设定获得最佳性能，而与其他AI系统竞争的设定表现最差。对于经过微调的模型，与自身历史表现竞争的设定效果最强，其次是与人类竞争，且性能进一步受到提示设计、模型选择与数据集特性之间耦合效应的影响。其次，不当的系统提示设计可能导致性能显著下降，其效果甚至逊于直接微调。第三，直接微调虽然在感知、预测和推理等语义层面指标上带来显著提升，但在动作准确性方面的改进有限。相比之下，我们提出的系统提示在动作准确性上产生了不成比例的更大提升，这表明所提出的提示设计主要发挥决策级约束作用，而非表征增强功能。