Haolin He
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A novel Token2Wav architecture is proposed to improve the efficiency of LLM-based TTS and multimodal systems.
This paper introduces Audio-Dependency Filtering (ADF) pipeline for Audio-Dependent Question Answering (ADQA) task in DCASE~2026, achieving top overall and sub-10B accuracy.
This paper introduces Omni Demand Understanding (ODU), a benchmark for evaluating AI assistants' ability to infer user demands from multimodal interaction.
The paper presents OmniVChat-Studio, a multi-agent data engine for synthesizing audio-visual dialogues, and OmniVChat-RL, a reinforcement learning reward design for evaluating omni models.
Papers
Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction
Qi Chen, Yunfei Chu, Haolin He, Yifan Yang +14 more
This paper introduces Omni Demand Understanding (ODU), a benchmark for evaluating AI assistants' ability to infer user demands from multimodal interaction.