activity
20242026
most citedMiMo-Audio: Audio Language Models are Few-Shot Learners

2 citations · 4 across the 13 of their papers we have counts for

collaborators

14 papers

cs.AI2026

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

Bowen Ye, Lei Li, Shicheng Li +16

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existi…

cs.CL2026

From Refuse to Richness: Rubric Rewards for Long-Form Hallucination Reinforcement Learning

Yudong Wang, Zhe Yang, Wenhan Ma +6

Rewards that penalize unsupported claims can improve grounding in long-form generation, but they can also teach models to answer less. We study this refusal-to-richness trade-off i…

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

From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory

Yishuo Cai, Xingyu Guo, Xuancheng Huang +8

Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to upda…

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.CV2025

GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation

Rang Li, Lei Li, Shuhuai Ren +10

Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language mod…

cs.CL2025

Enhancing Reliability across Short and Long-Form QA via Reinforcement Learning

Yudong Wang, Zhe Yang, Wenhan Ma +2

While reinforcement learning has unlocked unprecedented complex reasoning in large language models, it has also amplified their propensity for hallucination, creating a critical tr…

cs.CL2025

Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers

Wenhan Ma, Hailin Zhang, Liang Zhao +4

Reinforcement learning (RL) has emerged as a crucial approach for enhancing the capabilities of large language models. However, in Mixture-of-Experts (MoE) models, the routing mech…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.DB2025

Detecting Flow Gaps in Data Streams

Siyuan Dong, Yuxuan Tian, Wenhan Ma +5

Data stream monitoring is a crucial task which has a wide range of applications. The majority of existing research in this area can be broadly classified into two types, monitoring…

cs.CL2025

SCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios

Weidong Zhan, Yue Wang, Nan Hu +12

Currently, long-chain reasoning remains a key challenge for large language models (LLMs) because natural texts lack sufficient explicit reasoning data. However, existing benchmarks…

cs.LG2024

How to Craft Backdoors with Unlabeled Data Alone?

Yifei Wang, Wenhan Ma, Stefanie Jegelka +1

Relying only on unlabeled data, Self-supervised learning (SSL) can learn rich features in an economical and scalable way. As the drive-horse for building foundation models, SSL has…