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

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

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.IR2025

ComLQ: Benchmarking Complex Logical Queries in Information Retrieval

Ganlin Xu, Zhitao Yin, Linghao Zhang +6

Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries tha…

cs.AI2025

Solving the Min-Max Multiple Traveling Salesmen Problem via Learning-Based Path Generation and Optimal Splitting

Wen Wang, Xiangchen Wu, Liang Wang +3

This study addresses the Min-Max Multiple Traveling Salesmen Problem (-TSP), which aims to coordinate tours for multiple salesmen such that the length of the longest tour is m…

cs.CV2025

RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video

Shuhang Xun, Sicheng Tao, Jungang Li +11

Multimodal Large Language Models (MLLMs) have made rapid progress in perception, understanding, and reasoning, yet existing benchmarks fall short in evaluating these abilities unde…