collaborators

8 papers

cs.CL2026

Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs

Zixuan Ren, Jinliang Lu, Junhong Wu +5

Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language models (LLMs). Recent research…

cs.CV2026

PromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue

Zirui Zhang, Yaping Zhang, Lu Xiang +4

Document Layout Analysis (DLA) is crucial for document artificial intelligence and has recently received increasing attention, resulting in an influx of large-scale public DLA data…

cs.CV2026

ICDAR 2025 Competition on End-to-End Document Image Machine Translation Towards Complex Layouts

Yaping Zhang, Yupu Liang, Zhiyang Zhang +5

Document Image Machine Translation (DIMT) seeks to translate text embedded in document images from one language to another by jointly modeling both textual content and page layout,…

cs.AI2025

HiSciBench: A Hierarchical Multi-disciplinary Benchmark for Scientific Intelligence from Reading to Discovery

Yaping Zhang, Qixuan Zhang, Xingquan Zhang +8

The rapid advancement of large language models (LLMs) and multimodal foundation models has sparked growing interest in their potential for scientific research. However, scientific…

cs.CL2025

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities

Lu Xiang, Yang Zhao, Yaping Zhang +1

Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering inte…

cs.CL2025

Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency

Yupu Liang, Yaping Zhang, Zhiyang Zhang +5

Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document…