18 citations · 23 across the 14 of their papers we have counts for
17 papers
Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning
Ke Xu, Yuhao Wang, Ziyang Cheng +3
Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams…
From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench
Ke Xu, Yuhao Wang, Yu Wang
Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existing benchmarks primarily focus…
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…
Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
Shuyang Jiang, Yuhao Wang, Ya Zhang +2
Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting…
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…
CS3-Bench: Evaluating and Enhancing Speech-to-Speech LLMs for Mandarin-English Code-Switching
Heyang Liu, Yuhao Wang, Ziyang Cheng +4
The advancement of multimodal large language models has accelerated the development of speech-to-speech interaction systems. While natural monolingual interaction has been achieved…
VocalNet: Speech LLM with Multi-Token Prediction for Faster and High-Quality Generation
Yuhao Wang, Heyang Liu, Ziyang Cheng +4
Speech large language models (LLMs) have emerged as a prominent research focus in speech processing. We introduce VocalNet-1B and VocalNet-8B, a series of high-performance, low-lat…
Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusal
Yuhao Wang, Zhiyuan Zhu, Heyang Liu +4
Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trus…
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm
Hongcheng Liu, Yusheng Liao, Siqv Ou +4
The application of the Multi-modal Large Language Models (MLLMs) in medical clinical scenarios remains underexplored. Previous benchmarks only focus on the capacity of the MLLMs in…
Decoding Linguistic Representations of Human Brain
Yu Wang, Heyang Liu, Yuhao Wang +6
Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the brain. Decoding linguistic repres…
Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator
Yusheng Liao, Yutong Meng, Yuhao Wang +3
Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored. Previou…
MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception
Yuhao Wang, Yusheng Liao, Heyang Liu +3
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer…
SelfEvolve: A Code Evolution Framework via Large Language Models
Shuyang Jiang, Yuhao Wang, Yu Wang
Large language models (LLMs) have already revolutionized code generation, after being pretrained on publicly available code data. However, while various methods have been proposed…