@inproceedings{10.1007/978-981-95-5679-3_15,
  title = {SentiMM: A Multimodal Multi-agent Framework for Sentiment Analysis in Social Media},
  author = {Xu, Xilai and Zhao, Zilin and Song, Chengye and Wang, Zining and Qiang, Jinhe and Yan, Jiongrui and Lin, Yuhuai},
  year = {2026},
  booktitle = {Pattern Recognition and Computer Vision},
  editor = {Kittler, Josef and Xiong, Hongkai and Yang, Jian and Chen, Xilin and Lu, Jiwen and Lin, Weiyao and Yu, Jingyi and Zheng, Weishi},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  pages = {208--221},
  isbn = {978-981-95-5679-3},
  abstract = {With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enriches context via knowledge retrieval, and aggregates results for final sentiment classification. We also introduce SentiMMD, a large-scale multimodal dataset with seven fine-grained sentiment categories. Extensive experiments demonstrate that SentiMM achieves superior performance compared to state-of-the-art baselines, validating the effectiveness of our structured approach.},
}

@misc{feng2026seeingviewsbenchmarkingspatial,
  title = {Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes},
  author = {Zhiyuan Feng and Zhaolu Kang and Qijie Wang and Zhiying Du and Jiongrui Yan and Shubin Shi and Chengbo Yuan and Huizhi Liang and Yu Deng and Qixiu Li and Rushuai Yang and Arctanx An and Leqi Zheng and Weijie Wang and Shawn Chen and Sicheng Xu and Yaobo Liang and Jiaolong Yang and Baining Guo},
  year = {2026},
  eprint = {2510.19400},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2510.19400},
}