Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents

2025-07-29

AI Summary

This study addresses the significant degradation in user experience caused by response latency in LLM-driven virtual agents during free-form dialogue in VR environments. We propose real-time mitigation strategies based on natural conversational fillers—such as gesture cues and vocal feedback—to preserve interaction fluency. Through multi-scenario latency simulation and user studies, we establish that response delays exceeding 4 seconds markedly reduce Quality of Experience (QoE); conversely, well-timed fillers reduce perceived latency by up to 38%, sustaining conversational naturalness and immersion. We design an end-to-end deployable VR-LLM dialogue integration pipeline that enables low-overhead, highly compatible dynamic injection of filler strategies, and release the full toolchain as open source. Key contributions include: (1) the first empirical quantification of latency tolerance thresholds for VR-based conversational interfaces; (2) experimental validation of the compensatory effect of conversational fillers on perceived latency; and (3) the first open-source framework specifically optimized for mitigating LLM-induced latency in VR dialogue systems.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational fillers improve perceived response time, especially in high-delay conditions. Our findings provide insights for practitioners and researchers to optimize user engagement whenever conversational systems' responses are delayed by network limitations or slow hardware. We also contribute an open-source pipeline that streamlines deploying conversational agents in virtual environments.

Problem

• Mitigating response delays in LLM-powered VR conversations
• Evaluating conversational fillers for latency bridging
• Optimizing user engagement in delayed response scenarios

Innovation

• Using conversational fillers to bridge delays
• Evaluating effectiveness across latency levels
• Open-source pipeline for virtual agent deployment