3 citations · 14 across the 35 of their papers we have counts for
40 papers
-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
Heyang Liu, Jiayi Huang, Wenyang Xiao +8
The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seaml…
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Wenhao Wang, Peizhi Niu, Gongyi Zou +9
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly ado…
LaSR: Context-Aware Speech Recognition via Latent Reasoning
Heyang Liu, Ziyang Cheng, Jiayi Huang +5
Speech recognition in specialized domains requires leveraging contextual or topical information to improve the recognition of domain-specific entities. Speech Large Language Models…
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…
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…
AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
Yusheng Liao, Chuan Xuan, Yutong Cai +4
Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constra…
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…
Mining Useful General Data for Low-Resource Domain Adaptation
Pingjie Wang, Hongcheng Liu, Yusheng Liao +5
Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…
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…
Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling
Shuyang Jiang, Yusheng Liao, Ya Zhang +2
While large reasoning models trained with critic-free reinforcement learning and verifiable rewards (RLVR) represent the state-of-the-art, their practical utility is hampered by ``…
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Zhe Chen, Yusheng Liao, Zhiyuan Zhu +4
Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world…
Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning
Siqu Ou, Hongcheng Liu, Pingjie Wang +4
While chains-of-thought (CoT) have advanced complex reasoning in multimodal large language models (MLLMs), existing methods remain confined to text or static visual domains, often…
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…
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models
YiQiu Guo, Yuchen Yang, Zhe Chen +5
The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. E…
MedS: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision
Shuyang Jiang, Yusheng Liao, Zhe Chen +3
Medical language models face critical barriers to real-world clinical reasoning applications. However, mainstream efforts, which fall short in task coverage, lack fine-grained supe…
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications
Zhe Chen, Yusheng Liao, Shuyang Jiang +4
Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer he…
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…
ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents
Yusheng Liao, Shuyang Jiang, Yanfeng Wang +1
Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. However, current LL…
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…