2 citations · 3 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
ToolCritic: Detecting and Correcting Tool-Use Errors in Dialogue Systems
Hassan Hamad, Yingru Xu, Liang Zhao +2
Tool-augmented large language models (LLMs) are increasingly employed in real-world applications, but tool usage errors still hinder their reliability. We introduce ToolCritic, a d…
CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis
Xinzhe Xu, Liang Zhao, Hongshen Xu +1
Large Language Models (LLMs) are increasingly tasked with analyzing legal texts and citing relevant statutes, yet their reliability is often compromised by general pre-training tha…
MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
LLM-Core Xiaomi, :, Bingquan Xia +62
We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages. During pre-training, we enhance the data p…
MiMo-VL Technical Report
Core Team, Zihao Yue, Zhenru Lin +71
We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal rea…
The Wisdom of Agent Crowds: A Human-AI Interaction Innovation Ignition Framework
Senhao Yang, Qiwen Cheng, Ruiqi Ma +3
With the widespread application of large AI models in various fields, the automation level of multi-agent systems has been continuously improved. However, in high-risk decision-mak…