Realistic and diverse multi-agent driving scenes are crucial for evaluating autonomous vehicles. We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling from a scene generation model. Experiments on nuPlan and Waymo show that OMEGA improves generation realism, consistency, and controllability.
@inproceedings{li2025optimization,title={Optimization-Guided Diffusion for Interactive Scene Generation},author={Li, Shihao and Ye, Naisheng and Li, Tianyu and Chitta, Kashyap and An, Tuo and Su, Peng and Wang, Boyang and Liu, Haiou and Lv, Chen and Li, Hongyang},booktitle={arXiv preprint},year={2025},}
Cell Press
Iot-llm: Enhancing real-world iot task reasoning with large language models
This paper presents a novel approach to enhancing real-world IoT task reasoning with large language models. We demonstrate significant improvements in performance across various benchmarks.
@article{an2024iot,title={Iot-llm: Enhancing real-world iot task reasoning with large language models},author={An, Tuo and Zhou, Yunjiao and Zou, Han and Yang, Jianfei},journal={Patterns, Cell Press (Cover Paper)},year={2025},}
2024
EMNLP
EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
Shangyu Xing, Fei Zhao, Zhen Wu, and 5 more authors
In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination. To eliminate hallucinations, we propose an efficient fine-grained unlearning framework (EFUF), which can eliminate hallucinations without the need for paired data. Extensive experiments show that our method consistently reduces hallucinations while preserving the generation quality with modest computational overhead.
@inproceedings{xing2024efuf,title={EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models},author={Xing, Shangyu and Zhao, Fei and Wu, Zhen and An, Tuo and Chen, Weihao and Li, Chunhui and Zhang, Jianbing and Dai, Xinyu},booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},year={2024},}
