33 citations · 69 across the 28 of their papers we have counts for
12 papers · 1 filter
GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation
Kaichen Zhang, Yuzhong Hong, Junwei Bao +4
Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent advances in post-training met…
StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Keming Wu, Baoyi Wang, Kaichen Zhang +7
Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compressi…
Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation
Yueting Zhu, Yuehao Song, Kaicheng Zhang +5
Streaming video generation holds strong potential for world modeling, where future frames must be inferred online sequentially to form a continuous video stream. However, streaming…
Aero Realtime: Fully Aligned Input-Output Streams for Low-Latency Streaming Multimodal Generation
Kaichen Zhang, Wei Huang, Keming Wu +2
Existing streaming multimodal models process observations incrementally but still follow a turn-based prefill-then-decode pattern, making them non-duplex: new observations cannot n…
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
Senqiao Yang, Kaichen Zhang, Zhaoyang Jia +20
Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process…
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Xinjie Zhang, Peng Zhang, Shicheng Zheng +21
Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to…
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
Kaicheng Zhang, Wen Ge, Lei Jiang +5
Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial rele…
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Xiang An, Yin Xie, Feilong Tang +27
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…
ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning
Zuhao Yang, Kaichen Zhang, Sudong Wang +7
Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video unders…
EponaV2: Driving World Model with Comprehensive Future Reasoning
Jiawei Xu, Zhizhou Zhong, Zhijian Shu +8
Data scaling plays a pivotal role in the pursuit of general intelligence. However, the prevailing perception-planning paradigm in autonomous driving relies heavily on expensive man…
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
Trung Nguyen Quang, Yiming Gao, Fanyi Pu +3
When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perception or in action? Recent…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Keming Wu, Zuhao Yang, Kaichen Zhang +24
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…