most citedMiMo-Audio: Audio Language Models are Few-Shot Learners

2 citations · 4 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2026

HySparse2: Hybrid Sparse Attention with Two-Level KV Sharing

Jianyu Wei, Yizhao Gao, Qihao Zhang +12

Long-horizon and multi-turn agents typically generate short actions and process long observations from tools and environments. This growing context demands efficient prefill, compa…

cs.LG2026

OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models

Shijie Cao, Qingyu Zhang, Boxi Yu +6

Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-…

cs.CL2026

HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing

Yizhao Gao, Jianyu Wei, Qihao Zhang +11

This work introduces Hybrid Sparse Attention (HySparse), a new architecture that interleaves each full attention layer with several sparse attention layers. While conceptually simp…

cs.CL2026★ 1 cited

MiMo-V2-Flash Technical Report

Core Team, Bangjun Xiao, Bingquan Xia +123

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-…

cs.CL2025★ 2 cited

MiMo-Audio: Audio Language Models are Few-Shot Learners

Core Team, Dong Zhang, Gang Wang +97

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with…

cs.CL2025★ 1 cited

Data Efficacy for Language Model Training

Yalun Dai, Yangyu Huang, Xin Zhang +6

Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or opt…

cs.LG2025

SeerAttention-R: Sparse Attention Adaptation for Long Reasoning

Yizhao Gao, Shuming Guo, Shijie Cao +12

We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the…

cs.CL2025

Rectified Sparse Attention

Yutao Sun, Tianzhu Ye, Li Dong +6

Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignmen…