Weili Xu

I am a sophomore in Computer Engineering, currently pursuing a dual degree from University of Illinois Urbana-Champaign and Zhejiang University.

I am fortunate to collaborate with Wenhao Chai and Enxin Song, working on Efficient Long Video Understanding. We built AuroraLong, a hybrid MLLM that efficiently handles hour-long videos on a single consumer GPU while achieving comparable performance to its Transformer counterparts on multiple video understanding benchmarks such as MLVU, MovieChat-1k and VDC.

I’m interested in various aspects of machine learning and computer systems:

Hardware-aware efficient algorithms
Exploiting sparsity for training and inference acceleration
Applications of multi-modal (video, audio, text, etc.) long-context modeling

news

Jun 25, 2025	One paper accepted by ICCV 2025, see you in Hawaii!
Mar 31, 2025	One paper accepted by the second CVPR workshop on Efficient Large Vision Models
Jan 21, 2025	Start to work as Teaching Assistant for ECE 220 Computer Systems & Programming with Prof. Ujjal Bhowmik

selected publications

AuroraLong
Bringing RNNs Back to Efficient Open-Ended Video Understanding

Weili Xu, Enxin Song, Wenhao Chai, and 3 more authors

In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2025

Abs arXiv Bib PDF Supp

The challenge of long video understanding lies in its high computational complexity and prohibitive memory cost, since the memory and computation required by transformer-based LLMs scale quadratically with input sequence length. We propose AuroraLong to address this challenge by replacing the LLM component in MLLMs with a linear RNN language model that handles input sequence of arbitrary length with constant-size hidden states. To further increase throughput and efficiency, we combine visual token merge with linear RNN models by reordering the visual tokens by their sizes in ascending order. Despite having only 2B parameters and being trained exclusively on public data, AuroraLong achieves performance comparable to Transformer-based models of similar size trained on private datasets across multiple video benchmarks. This demonstrates the potential of efficient, linear RNNs to democratize long video understanding by lowering its computational entry barrier. To our best knowledge, we are the first to use a linear RNN based LLM backbone in a LLaVA-like model for open-ended video understanding.
@inproceedings{xu2025auroralong, title = {Bringing RNNs Back to Efficient Open-Ended Video Understanding}, author = {Xu, Weili and Song, Enxin and Chai, Wenhao and Wen, Xuexiang and Ye, Tian and Wang, Gaoang}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = oct, year = {2025}, }