most citedCURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Persona Prompting as a Lens on LLM Social Reasoning

Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov +3

For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. W…

cs.CY2025

Insights from the ICLR Peer Review and Rebuttal Process

Amir Hossein Kargaran, Nafiseh Nikeghbal, Jing Yang +1

Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and…

cs.CV2025

LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training

Xiang An, Yin Xie, Kaicheng Yang +20

We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial co…

cs.LG20251 cited

CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention

Qingbin Li, Rongkun Xue, Jie Wang +8

Recent advances in Reinforcement Learning with Verified Reward (RLVR) have driven the emergence of more sophisticated cognitive behaviors in large language models (LLMs), thereby e…

cs.DL2025

Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process

Jing Yang

The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review pla…