Papers by Junpeng Ma
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
Copied to clipboard
Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao
| Challenge: | generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap. |
| Approach: | They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback . |
| Outcome: | The proposed system significantly improves image reasoning and generation quality. |
Summarizing Dialogues with Negative Cues (2022.coling-1)
Copied to clipboard
| Challenge: | Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. |
| Approach: | They propose to have the model perceive the redundant parts of an input dialogue history during the training phase. |
| Outcome: | The proposed method significantly outperforms baselines on the semantic matching and factual consistent based metrics. |
Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that Video Large Language Models (Vide-oLLMs) are efficient at video understanding but lack the quadratic complexity of visual tokens. |
| Approach: | They propose a plug-and-play inference acceleration framework for VideoLLM token compression that quantifies each frame’s uniqueness and adaptively adjusts compression intensity across frames. |
| Outcome: | Extensive experiments on video large language models and benchmarks show that the proposed framework can preserve essential information while reducing redundancy in video sequences. |
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)
Copied to clipboard
Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E
| Challenge: | AEGIS examines whether current models can effectively audit AI-generated images in academic papers. |
| Approach: | They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics. |
| Outcome: | AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis. |