activity
20232026
most citedMiMo-Audio: Audio Language Models are Few-Shot Learners

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD2026

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

Zihan Zhang, Xize Cheng, Wenhao Yan +5

Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level prec…

cs.LG2026

X-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment

Dongjie Fu, Di Cao, Xize Cheng +6

While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primar…

cs.CL20261 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.CL20252 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.CL20231 cited

Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting

Hejie Cui, Xinyu Fang, Zihan Zhang +7

Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., su…