Impressive work on achieving sub-second latency for real-time AI video interactions! Switching from a NeRF-based backbone to Gaussian Splatting in your Phoenix-2 model seems like a clever optimization for faster frame generation on lower-end hardware. I'm particularly interested in how you tackled the time-to-first-token (TTFT) latency with LLMs—did you implement any specific techniques to reduce it, like model pruning or quantization? Also, your approach to accurate end-of-turn detection in conversations is intriguing. Could you share more about the models or algorithms you used to predict conversational cues without adding significant latency? Balancing latency, scalability, and cost in such a system is no small feat; kudos to the team!