1 citations · 2 across the 7 of their papers we have counts for
10 papers · 1 filter
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
Hongcheng Liu, Yuhao Wang, Zhe Chen +5
Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring syn…
VocalNet-MDM: Accelerating Streaming Speech LLM via Self-Distilled Masked Diffusion Modeling
Ziyang Cheng, Yuhao Wang, Heyang Liu +4
Recent Speech Large Language Models~(LLMs) have achieved impressive capabilities in end-to-end speech interaction. However, the prevailing autoregressive paradigm imposes strict se…
VocalBench-zh: Decomposing and Benchmarking the Speech Conversational Abilities in Mandarin Context
Heyang Liu, Ziyang Cheng, Yuhao Wang +6
The development of multi-modal large language models (LLMs) leads to intelligent approaches capable of speech interactions. As one of the most widely spoken languages globally, Man…
VocalNet-M2: Advancing Low-Latency Spoken Language Modeling via Integrated Multi-Codebook Tokenization and Multi-Token Prediction
Yuhao Wang, Ziyang Cheng, Heyang Liu +4
Current end-to-end spoken language models (SLMs) have made notable progress, yet they still encounter considerable response latency. This delay primarily arises from the autoregres…
When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs
Hongcheng Liu, Pingjie Wang, Yuhao Wang +3
Multimodal large language models (MLLMs) have shown strong capabilities across a broad range of benchmarks. However, most existing evaluations focus on passive inference, where mod…
VocalBench-DF: A Benchmark for Evaluating Speech LLM Robustness to Disfluency
Hongcheng Liu, Yixuan Hou, Heyang Liu +3
While Speech Large Language Models (Speech-LLMs) show strong performance in many applications, their robustness is critically under-tested, especially to speech disfluency. Existin…