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20242026
most citedERNIE 5.0 Technical Report

2 citations · 3 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling

Manwen Liao, Xinyu Lian, Jian Mao +11

Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblie…

cs.GR2026

MeshFlow: Mesh Generation with Equivariant Flow Matching

Qi Sun, Kiyohiro Nakayama, Jing Nathan Yan +6

Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important symmetries, including permuta…

cs.IR2026

LLM Retrieval for Stable and Predictable Ad Recommendations

Vinodh Kumar Sunkara, Satheeshkumar Karuppusamy, Hangjun Xu +13

Traditional ads recommendation systems have primarily focused on optimizing for prediction accuracy of click or conversion events using canonical metrics such as recall or normaliz…

cs.CV2026

Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis

Yixuan Yang, Zhen Luo, Wanshui Gan +5

Designing realistic and functional 3D indoor rooms is essential for a wide range of applications, including interior design, virtual reality, gaming, and embodied AI. While recent…

cs.CL2026★ 2 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…