Papers by Junxian Cai
SpecEdit: A Spectral Approach for Multi-Round Knowledge Editing (2026.findings-acl)
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| Challenge: | Multi-round knowledge editing suffers from performance degradation as edits accumulate . intrinsic knowledge of model and historical edit memories are naively coupled during editing . SpecEdit improves model editing performance by reducing destructive coupling . |
| Approach: | They propose a spectral-based model editing module that integrates into existing editing methods without altering their original optimization procedures. |
| Outcome: | The proposed model improves performance on multiple LLMs and editing methods. |
VRoPE: Rotary Position Embedding for Video Large Language Models (2025.emnlp-main)
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Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, Jing Liu
| Challenge: | Existing versions of Large Language Models (LLMs) lack a positional encoding strategy for video. |
| Approach: | They propose a new positional encoding method tailored for Video-LLMs that mitigates positional biases and ensures a more uniform distribution of spatial focus. |
| Outcome: | The proposed method outperforms existing versions of RoPE in video understanding and reasoning tasks. |
Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)
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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing models for knowledge editing focus on knowledge-level or static visual domains, overlooking dynamic semantics. |
| Approach: | They propose a benchmark for modeling large language models using six representative models . they analyze the strengths and limitations of existing models and identify new directions . |
| Outcome: | The proposed benchmark extends existing models from static modalities to dynamic video scenarios. |
Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies (2025.acl-long)
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| Challenge: | Existing scaling laws suggest augmenting model size and training data results in enhanced performance, but recent studies reveal deviations, particularly in large language models, where performance improvements decelerate—a phenomenon known as sub-scaling. |
| Approach: | They propose a sub-optimal scaling law that better predicts performance in sub-scaling regimes by examining data quality and training strategies. |
| Outcome: | The proposed scaling law better predicts performance in sub-scaling regimes, highlighting the importance of data quality and diversity. |