most citedEmerging Properties in Unified Multimodal Pretraining

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

BlenderWorld: Benchmarking Visual Agents for 3D Creation from Real-World References

Xinyu Fang, Yuchi Wang, Weihao Yu +12

Recent advances in multimodal large language models (MLLMs) have enabled them to act as autonomous agents, creating and editing 3D models via code or direct software manipulation.…

cs.CV2025

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

Yuwei Niu, Weiyang Jin, Jiaqi Liao +7

Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal…

cs.CV2025

LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation

Zeyu Wang, Zilong Chen, Chenhui Gou +8

Unified multimodal models have recently shown remarkable gains in both capability and versatility, yet most leading systems are still trained from scratch and require substantial c…

cs.CL2025

Artificial Hippocampus Networks for Efficient Long-Context Modeling

Yunhao Fang, Weihao Yu, Shu Zhong +3

Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in atten…

cs.CV2025★ 1 cited

Seed1.5-VL Technical Report

Dong Guo, Faming Wu, Feida Zhu +194

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…

cs.CV2025★ 1 cited

Emerging Properties in Unified Multimodal Pretraining

Chaorui Deng, Deyao Zhu, Kunchang Li +9

Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. In this work, we introduce BAGEL, an open-source foundationa…