Mengye Ren

General areas: machine learning, computer vision, representation learning, continual learning, meta-learning, few-shot learning, artificial intelligence. My key research question is: how do we enable human-like, agent-based machine intelligence to continually learn, adapt, and reason in naturalistic environments? I am interested in the emergence of intelligence by learning from a point-of-view experience. Current research topics in my lab are: Visual representation learning and planning in the wild; Adaptive agents and foundation models; Few-shot concept learning, reasoning, and abstraction.

[2026] The self requires learning. Mengye Ren. Preprint, 2026. [2026] Seeking the unfamiliar but memorable: Conceptual creativity as meta-learning. Mengye Ren. arXiv preprint arXiv:2605.16477, 2026. [2026] Memory Transfer Learning: How memories are transferred across domains in coding agents. Kangsan Kim, Minki Kang, Taeil Kim, Yanlai Yang, Mengye Ren, Sung Ju Hwang. arXiv preprint arXiv:2604.14004, 2026. [2026] BabyCLIP: Continual language grounding through the experience of a single child. Xiaoyang Jiang, Yanlai Yang, Kenneth A. Norman, Brenden Lake, Mengye Ren. CVPR 2026 Workshop on Computer Vision for Children, 2026. [2026] StreamMem: Query-agnostic KV cache memory for streaming video understanding. Yanlai Yang, Zhuokai Zhao, Satya Narayan Shukla, Aashu Singh, Shlok Kumar Mishra, Lizhu