most citedERNIE 5.0 Technical Report

2 citations · 2 across the 2 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.CL2026

PTCBENCH: Benchmarking Contextual Stability of Personality Traits in LLM Systems

Jiongchi Yu, Yuhan Ma, Xiaoyu Zhang +4

With the increasing deployment of large language models (LLMs) in affective agents and AI systems, maintaining a consistent and authentic LLM personality becomes critical for user…

cs.CV2025

Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation

Ziyu Ye, Chen Ju, Chaofan Ma +1

Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Sinc…

cs.CV2025

Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation

Yiguo He, Junjie Zhu, Yiying Li +5

The application of Vision-language foundation models (VLFMs) to remote sensing (RS) imagery has garnered significant attention due to their superior capability in various downstrea…

cs.LG2025

Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?

Zijian Zhao, Dian Jin, Zijing Zhou +1

Stage lighting is a vital component in live music performances, shaping an engaging experience for both musicians and audiences. In recent years, Automatic Stage Lighting Control (…