most citedERNIE 5.0 Technical Report

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

collaborators

7 papers

cs.CL2026

Beyond Global Scalars: Synergizing Token-Level Statistics and Deep Semantics for Adversarial AIGC Text Detection

Peiming Li, Yifan Wang, Zhiyuan Hu +3

The rapid evolution of large language models necessitates robust machine-generated text detection. Existing paradigms typically follow two isolated tracks. Training-free methods re…

cs.CV2026

ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space

Peiming Li, Yifan Wang, Xiaotian Zhang +4

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step perception and logical deduct…

cs.AI2026

WebForge: Breaking the Realism-Reproducibility-Scalability Trilemma in Browser Agent Benchmark

Peng Yuan, Yuyang Yin, Yuxuan Cai +1

Existing browser agent benchmarks face a fundamental trilemma: real-website benchmarks lack reproducibility due to content drift, controlled environments sacrifice realism by omitt…

cs.CL2026

HMS-BERT: Hybrid Multi-Task Self-Training for Multilingual and Multi-Label Cyberbullying Detection

Zixin Feng, Xinying Cui, Yifan Sun +5

Cyberbullying on social media is inherently multilingual and multi-faceted, where abusive behaviors often overlap across multiple categories. Existing methods are commonly limited…

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.CL2026

Yunque DeepResearch Technical Report

Yuxuan Cai, Xinyi Lai, Peng Yuan +8

Deep research has emerged as a transformative capability for autonomous agents, empowering Large Language Models to navigate complex, open-ended tasks. However, realizing its full…