等一等：VR中LLM智能体延迟反馈机制的分组件分析
Wait for It: A Component-Level Analysis of Latency Feedback Mechanisms for LLM Agents in VR
一项18人被试内研究比较了VR中LLM智能体响应延迟时的三种反馈方式：言语填充（如“嗯……”“呃……”）、视觉指示（加载转圈）和具身手势（思考动画）。依据摘要，具身手势在多个维度上显著优于视觉加载指示，使智能体更显真实、更具社会临场感；而加载转圈等显式视觉反馈反而降低了感知拟人度、破坏沉浸感，研究者认为这使用户从社会心智模型转向技术心智模型。
arXiv:2610.23336v1 Announce Type: new Abstract: Delays in AI responses pose a significant challenge to immersive Virtual Reality experiences. While previous research demonstrates that feedback can reduce the perception of waiting, we lack understanding of which types work best and why. We conducted a within-subjects study with 18 participants to systematically compare three isolated feedback approaches during VR agent interactions: verbal fillers (e.g., um..., hmm...), visual indicators (loading spinners), and embodied gestures (thinking animations). Our results revealed that embodied gestures significantly outperformed visual loading indicators across multiple dimensions, making agents feel more lifelike and socially present. Interestingly, explicit visual feedback like loading spinners actually degraded perceived humanness and broke immersion by forcing users to shift from social to technical mental models. These findings suggest that natural, behavior-based feedback maintains the illusion of conversing with a thinking entity, while technical indicators expose the computational substrate. We present empirically-grounded design guidelines for creating more engaging AI-powered VR agents.
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来源：arXiv · Human-Computer Interaction · arxiv.org