DLIOS: An LLM-Augmented Real-Time Multi-Modal Interactive Enhancement Overlay System for Douyin Live Streaming

Abstract

We present DLIOS, a Large Language Model (LLM)-augmented real-time multi-modal interactive enhancement overlay system for Douyin (TikTok) live streaming. DLIOS employs a three-layer transparent window architecture for independent rendering of danmaku (scrolling text), gift and like particle effects, and VIP entrance animations, built around an event-driven WebView2 capture pipeline and a thread-safe event bus. On top of this foundation we contribute an LLM broadcast automation framework comprising: (1) a per-song four-segment prompt scheduling system (T1 opening/transition, T2 empathy, T3 era story/production notes, T4 closing) that generates emotionally coherent radio-style commentary from lyric metadata; (2) a JSON-serializable RadioPersonaConfig schema supporting hot-swap multi-persona broadcasting; (3) a real-time danmaku quick-reaction engine with keyword routing to static urgent speech or LLM-generated empathetic responses; and (4) the Suwan Li AI singer-songwriter persona case study -- over 100 AI-generated songs produced with Suno. A 36-hour stress test demonstrates: zero danmaku overlap, zero deadlock crashes, gift effect P95 latency <= 180 ms, LLM-to-TTS segment P95 latency <= 2.1 s, and TTS integrated loudness gain of 9.5 LUFS. live streaming; danmaku; large language model; prompt engineering; virtual persona; WebView2; WINMM; TTS; Suno; loudness normalization; real-time scheduling