@misc{li2025sculptorempoweringllmscognitive,title={Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management},author={Li, Mo and Xu, L. H. and Tan, Qitai and Ma, Long and Cao, Ting and Liu, Yunxin},year={2025},eprint={2508.04664},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2508.04664},journal={arXiv Preprint}}
NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities
Mo Li, Songyang Zhang, Taolin Zhang, Haodong Duan, Yunxin Liu, and Kai Chen
Transactions on Machine Learning Research (TMLR), 2025
@article{li2025needlebench,title={NeedleBench: Evaluating {LLM} Retrieval and Reasoning Across Varying Information Densities},author={Li, Mo and Zhang, Songyang and Zhang, Taolin and Duan, Haodong and Liu, Yunxin and Chen, Kai},journal={Transactions on Machine Learning Research (TMLR)},issn={2835-8856},year={2025},url={https://openreview.net/forum?id=cEvmIKsRw0},}
SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series
Qitai Tan*, Yiyun Chen*, Mo Li*, Ruiwen Gu, Yilin Su, and Xiao-Ping Zhang
In Proceeding of Advances in Neural Information Processing Systems (NeurIPS), 2025
@inproceedings{tan2025syntsbench,title={Syn{TSB}ench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series},author={Tan, Qitai and Chen, Yiyun and Li, Mo and Gu, Ruiwen and Su, Yilin and Zhang, Xiao-Ping},booktitle={Proceeding of Advances in Neural Information Processing Systems (NeurIPS)},year={2025},url={https://openreview.net/forum?id=rNw128KrYU},}
MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence
Sihan Yang, Runsen Xu, Yiman Xie, Sizhe Yang, Mo Li, Jingli Lin, Chenming Zhu, Xiaochen Chen, Haodong Duan, Xiangyu Yue, Dahua Lin, Tai Wang, and Jiangmiao Pang
@misc{yang2025mmsibenchbenchmarkmultiimagespatial,title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},author={Yang, Sihan and Xu, Runsen and Xie, Yiman and Yang, Sizhe and Li, Mo and Lin, Jingli and Zhu, Chenming and Chen, Xiaochen and Duan, Haodong and Yue, Xiangyu and Lin, Dahua and Wang, Tai and Pang, Jiangmiao},year={2025},eprint={2505.23764},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2505.23764},booktitle={arXiv Preprint}}
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
Maosong Cao, Taolin Zhang, Mo Li, Chuyu Zhang, Yunxin Liu, Conghui He, Haodong Duan, Songyang Zhang, and Kai Chen
In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Jul 2025
The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, the availability of high-quality human-annotated SFT data has become a significant bottleneck for LLMs, necessitating a greater reliance on synthetic training data. In this work, we introduce Condor, a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale. Our experimental results demonstrate that a base model fine-tuned on only 20K Condor-generated samples achieves superior performance compared to instruct model trained with RLHF. The additional refinement stage in Condor further enables iterative self-improvement for LLMs at various scales (up to 72B), validating the effectiveness of our approach. Furthermore, our investigation into the scaling of synthetic data in post-training reveals substantial unexplored potential for performance improvements, opening promising avenues for future research.
@inproceedings{maosongcao-etal-2025-condor,title={Condor: Enhance {LLM} Alignment with Knowledge-Driven Data Synthesis and Refinement},author={Cao, Maosong and Zhang, Taolin and Li, Mo and Zhang, Chuyu and Liu, Yunxin and He, Conghui and Duan, Haodong and Zhang, Songyang and Chen, Kai},editor={Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},booktitle={Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)},month=jul,year={2025},address={Vienna, Austria},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2025.acl-long.1091/},doi={10.18653/v1/2025.acl-long.1091},pages={22392--22412},isbn={979-8-89176-251-0},}
2024
InternLM2 Technical Report
Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, Xiaoyi Dong, Haodong Duan, Qi Fan, Zhaoye Fei, Yang Gao , and 85 more authors
@misc{cai2024internlm2technicalreport,title={InternLM2 Technical Report},author={Cai, Zheng and Cao, Maosong and Chen, Haojiong and Chen, Kai and Chen, Keyu and Chen, Xin and Chen, Xun and Chen, Zehui and Chen, Zhi and Chu, Pei and Dong, Xiaoyi and Duan, Haodong and Fan, Qi and Fei, Zhaoye and Gao, Yang and Ge, Jiaye and Gu, Chenya and Gu, Yuzhe and Gui, Tao and Guo, Aijia and Guo, Qipeng and He, Conghui and Hu, Yingfan and Huang, Ting and Jiang, Tao and Jiao, Penglong and Jin, Zhenjiang and Lei, Zhikai and Li, Jiaxing and Li, Jingwen and Li, Linyang and Li, Shuaibin and Li, Wei and Li, Yining and Liu, Hongwei and Liu, Jiangning and Hong, Jiawei and Liu, Kaiwen and Liu, Kuikun and Liu, Xiaoran and Lv, Chengqi and Lv, Haijun and Lv, Kai and Ma, Li and Ma, Runyuan and Ma, Zerun and Ning, Wenchang and Ouyang, Linke and Qiu, Jiantao and Qu, Yuan and Shang, Fukai and Shao, Yunfan and Song, Demin and Song, Zifan and Sui, Zhihao and Sun, Peng and Sun, Yu and Tang, Huanze and Wang, Bin and Wang, Guoteng and Wang, Jiaqi and Wang, Jiayu and Wang, Rui and Wang, Yudong and Wang, Ziyi and Wei, Xingjian and Weng, Qizhen and Wu, Fan and Xiong, Yingtong and Xu, Chao and Xu, Ruiliang and Yan, Hang and Yan, Yirong and Yang, Xiaogui and Ye, Haochen and Ying, Huaiyuan and Yu, Jia and Yu, Jing and Zang, Yuhang and Zhang, Chuyu and Zhang, Li and Zhang, Pan and Zhang, Peng and Zhang, Ruijie and Zhang, Shuo and Zhang, Songyang and Zhang, Wenjian and Zhang, Wenwei and Zhang, Xingcheng and Zhang, Xinyue and Zhao, Hui and Zhao, Qian and Zhao, Xiaomeng and Zhou, Fengzhe and Zhou, Zaida and Zhuo, Jingming and Zou, Yicheng and Qiu, Xipeng and Qiao, Yu and Lin, Dahua},year={2024},eprint={2403.17297},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2403.17297},journal={arXiv Preprint}}
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Haodong Duan, Xinyu Fang, Junming Yang, Xiangyu Zhao, Yuxuan Qiao, Mo Li, Amit Agarwal, Zhe Chen, Lin Chen, Yuan Liu, Yubo Ma, Hailong Sun, Yifan Zhang, Shiyin Lu, Tack Hwa Wong , and 17 more authors
In Proceeding of The 32nd ACM International Conference on Multimedia (ACM MM), Jul 2024
@inproceedings{duan2024vlmevalkitopensourcetoolkitevaluating,title={VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models},author={Duan, Haodong and Fang, Xinyu and Yang, Junming and Zhao, Xiangyu and Qiao, Yuxuan and Li, Mo and Agarwal, Amit and Chen, Zhe and Chen, Lin and Liu, Yuan and Ma, Yubo and Sun, Hailong and Zhang, Yifan and Lu, Shiyin and Wong, Tack Hwa and Wang, Weiyun and Zhou, Peiheng and Li, Xiaozhe and Fu, Chaoyou and Cui, Junbo and Chen, Jixuan and Song, Enxin and Mao, Song and Ding, Shengyuan and Liang, Tianhao and Zhang, Zicheng and Dong, Xiaoyi and Zang, Yuhang and Zhang, Pan and Wang, Jiaqi and Lin, Dahua and Chen, Kai},year={2024},eprint={2407.11691},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2407.11691},booktitle={Proceeding of The 32nd ACM International Conference on Multimedia (ACM MM)}}
2023
OpenCompass: A Universal Evaluation Platform for Foundation Models
@misc{2023opencompass,title={OpenCompass: A Universal Evaluation Platform for Foundation Models},author={Contributors, OpenCompass},url={https://github.com/open-compass/opencompass},year={2023},journal={Github Repository}}
Sculptor: Empowering LLMs with Cognitive Agency via Active Context ManagementarXiv Preprint, 2025
