collaborators

7 papers

cs.CV2026

AV-SyncBench: Decoupled Benchmarking of Temporal and Semantic Audio-Visual Synchronization

Tianhong Zhou, Mingyang Han, Boyu Li +8

Audio-visual feature extraction is a fundamental component of multimodal understanding and generation tasks. However, existing evaluation protocols for feature extraction models ex…

cs.SD2026

FreeSonic: Training-Free Temporal-Aware Decoupled Attention for Precise Audio Editing

Yuxuan Jiang, Mingyang Han, Yusheng Dai +12

Text-to-audio (TTA) generation has made significant strides, yet achieving precise and consistent audio editing remains a major challenge. However, existing methods struggle to bal…

cs.CL2026

YOCO++: Enhancing YOCO with KV Residual Connections for Efficient LLM Inference

You Wu, Ziheng Chen, Yizhen Zhang +6

Cross-layer key-value (KV) compression has been found to be effective in efficient inference of large language models (LLMs). Although they reduce the memory consumption of the KV…

cs.LG2026

Expert Divergence Learning for MoE-based Language Models

Jiaang Li, Haibin Chen, Langming Liu +9

The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where experts learn redundant functi…

cs.LG2026

SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion

Chengting Yu, Xiaobo Shu, Yadao Wang +8

Recursive (looped) Transformers decouple computational depth from parameter depth by repeatedly applying shared layers, providing an explicit architectural primitive for iterative…

cs.CL2026

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models

Zanlin Ni, Shenzhi Wang, Yang Yue +8

Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility i…

cs.LG2025

MeSH: Memory-as-State-Highways for Recursive Transformers

Chengting Yu, Xiaobo Shu, Yadao Wang +8

Recursive transformers reuse parameters and iterate over hidden states multiple times, decoupling compute depth from parameter depth. However, under matched compute, recursive mode…