2 citations · 5 across the 15 of their papers we have counts for
20 papers
VideoEvolve: Co-Evolving Memory and Retrieval for Long Video Understanding
Yongchao Xu, Bowen Ye, Jiefeng Gan +5
Long video understanding increasingly relies on external memory to organize massive visual streams into compact representations. However, most memory-based methods dynamically adap…
Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
Bowen Ye, Yongchao Xu, Junkai Ma +2
Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experience remains limited. Althou…
Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay
Haotian Chen, Bowen Ye, Yuning Zhang +1
Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a typed snapshot-settlement cont…
Groupwise Agentic Grading and Advantage Redistribution for Code Agent RL
Jinhao Dong, Liang Zhao, Zihao Yue +7
Reinforcement learning (RL) for code agents often uses executable tests to provide binary rewards. With these rewards, Group Relative Policy Optimization (GRPO) assigns identical a…
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…
PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems
Hanglong Lv, Dawei Zhu, Lei Li +11
Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally complete, single-turn queries. O…
CoEvo-Mem: Co-Evolving Retrieval Policy and Memory Bank for LLM Agents
Bowen Ye, Yongchao Xu, Zhijian Li +3
As memories accumulate across tasks and sessions, the performance of long-term LLM agents depends jointly on query-specific retrieval and continual memory refinement. However, exis…
Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning
Qianlong Yang, Bowen Ye, Xianda Guo +4
Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instruction tuning, internal MLLM rep…
Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
Weimin Xiong, Shuhao Gu, Bowen Ye +5
Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarc…
ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning
Bowen Ye, Zhijian Li, Junyue Huang +2
Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has been widely used for the verifi…
Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents
Bowen Ye, Rang Li, Qibin Yang +10
Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by…
Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
Chenxin Li, Zhengyang Tang, Mingxin Huang +8
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at…
Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems
Yaxuan Wang, Yifan Xiang, Ke Li +5
We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi-robot systems that requires zero training, zero prior knowledge, and zero simulation.…
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-…
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…
Proof2Hybrid: Automatic Mathematical Benchmark Synthesis for Proof-Centric Problems
Yebo Peng, Yaoming Li, Zixiang Liu +6
Evaluating the mathematical capability of Large Language Models (LLMs) is a critical yet challenging frontier. Existing benchmarks fall short, particularly for proof-centric proble…
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…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
Peizhuang Cong, Aomufei Yuan, Shimao Chen +4
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating…