most citedERNIE 5.0 Technical Report

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

collaborators

5 papers

cs.CL20262 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…

cs.CV2025

See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering

Junjie Wang, Yunhan Tang, Yijie Wang +4

Multimodal Large Language Models (MLLMs) have pushed the frontiers of Knowledge-Based Visual Question Answering (KBVQA), yet their reasoning is fundamentally bottlenecked by a reli…

cs.LG2025

SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot

Kaiwen Tuo, Huan Wang

State-space language models such as Mamba match Transformer quality while permitting linear complexity inference, yet still comprise billions of parameters that hinder deployment.…

cs.CV2025

FreeBlend: Advancing Concept Blending with Staged Feedback-Driven Interpolation Diffusion

Yufan Zhou, Haoyu Shen, Huan Wang

Concept blending is a promising yet underexplored area in generative models. While recent approaches, such as embedding mixing and latent modification based on structural sketches,…

cs.CV2025

Dynamic Token Reduction during Generation for Vision Language Models

Xiaoyu Liang, Chaofeng Guan, Jiaying Lu +3

Vision-Language Models (VLMs) have achieved notable success in multimodal tasks but face practical limitations due to the quadratic complexity of decoder attention mechanisms and a…