7 papers
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
Yuntian Tang, Bohan Jia, Wenxuan Huang +7
Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. E…
ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation
Shizhe Zhou, Bohan Jia, Kai Wu +4
While multimodal large language models (MLLMs) have achieved rapid progress in vision-language understanding, they remain prone to multimodal hallucinations, producing responses th…
CompBench: Benchmarking Complex Instruction-guided Image Editing
Bohan Jia, Wenxuan Huang, Yuntian Tang +14
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack com…
MASA: Rethinking the Representational Bottleneck in LoRA with Multi-A Shared Adaptation
Qin Dong, Yuntian Tang, Heming Jia +7
Low-Rank Adaptation (LoRA) has emerged as a dominant method in Parameter-Efficient Fine-Tuning (PEFT) for large language models, which augments the transformer layer with one down-…
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
Wenxuan Huang, Bohan Jia, Zijie Zhai +7
DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore…
DF-LLaVA: Unlocking MLLMs for Synthetic Image Detection via Knowledge Injection and Conflict-Driven Self-Reflection
Zhuokang Shen, Kaisen Zhang, Bohan Jia +4
With the increasing prevalence of synthetic images, evaluating image authenticity and locating forgeries accurately while maintaining human interpretability remains a challenging t…