1 citations · 1 across the 9 of their papers we have counts for
10 papers
VoiceMem: Streaming Dual-Brain Memory for Real-Time Interaction
Zhifei Xie, Jiaqi Lang, Ze An +7
Conversational systems, such as duplex speech language models (SLMs), still lack a streaming, accurate, and empathetic memory system as their soul. We introduce VoiceMem, a simple…
Audio Interaction Model
Zhifei Xie, Zihang Liu, Ze An +8
Audio is continuous and interactive, yet most Large Audio Language Models (LALMs) remain offline and streaming systems usually specialize in ASR or spoken dialogue. We formalize th…
Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models
Zhifei Xie, Mingbao Lin, Zihang Liu +3
Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio…
Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration
Hai Ye, Mingbao Lin, Hwee Tou Ng +1
Scaling laws for inference compute in multi-agent systems remain under-explored compared to single-agent scenarios. This work aims to bridge this gap by investigating the problem o…
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
Wengyi Zhan, Mingbao Lin, Shuicheng Yan +1
We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent inf…
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
Quanjian Song, Mingbao Lin, Wengyi Zhan +3
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models. It operates without the need for training, offering a distinct advanta…
EasyInv: Toward Fast and Better DDIM Inversion
Ziyue Zhang, Mingbao Lin, Shuicheng Yan +1
This paper introduces EasyInv, an easy yet novel approach that significantly advances the field of DDIM Inversion by addressing the inherent inefficiencies and performance limitati…
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement
Jiahao Ying, Mingbao Lin, Yixin Cao +5
This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller targ…
UIO-LLMs: Unbiased Incremental Optimization for Long-Context LLMs
Wenhao Li, Mingbao Lin, Yunshan Zhong +2
Managing long texts is challenging for large language models (LLMs) due to limited context window sizes. This study introduces UIO-LLMs, an unbiased incremental optimization approa…
Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models
Jiahao Ying, Yixin Cao, Yushi Bai +7
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for large…