--- license: mit library_name: transformers pipeline_tag: robotics tags: - embodied-ai - reinforcement-learning - multimodal-llm - computer-vision ---

Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration CVPR 2026

## MemoryExplorer **MemoryExplorer** is a multimodal large language model (MLLM) framework designed for Long-term Memory Embodied Exploration (LMEE). It unifies an agent's exploratory cognition and decision-making behaviors to promote lifelong learning in complex environments. The model is fine-tuned through reinforcement learning to encourage active memory querying using a multi-task reward function that includes action prediction, frontier selection, and memory-based question answering. ### Resources - **Paper:** [Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration](https://huggingface.co/papers/2601.10744) - **Project Page:** [https://wangsen99.github.io/papers/lmee/](https://wangsen99.github.io/papers/lmee/) - **Repository:** [https://github.com/wangsen99/LMEE](https://github.com/wangsen99/LMEE) - **Benchmark:** [LMEE-Bench](https://huggingface.co/datasets/wangsen99/LMEE-Bench) ## Citation If you find this work useful, please consider citing: ```bibtex @inproceedings{wang2026explore, title={Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration}, author={Wang, Sen and Liu, Bangwei and Gao, Zhenkun and Ma, Lizhuang and Wang, Xuhong and Xie, Yuan and Tan, Xin}, booktitle={Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR)}, year={2026} } ```