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

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 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.CL2026

MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training

Wenhan Ma, Jianyu Wei, Liang Zhao +10

Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabilities into one model remains…

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…