2026

RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models

Neural-symbolic Completed method paper

Yang Yang*, Hua XU*, Zhangyi Hu*, Yutao Yue (* equal contribution)

43rd International Conference on Machine Learning (ICML) 2026 Accepted

Summary This work constructs explainable rules for scientific discovery using LLMs. Specifically, we develop a unified framework integrating LLMs with probabilistic modeling through four stages: rule generation via LLM, weight learning through logistic regression, iterative refinement, and evaluation, achieving higher rule quality and better rule combination effects.

My role Equal-contribution core contributor; worked on probabilistic integration for combining LLM-generated rules, evaluation design, and paper writing.

[arXiv]

A Very Big Video Reasoning Suite

Cognitive Modeling Benchmark contribution

Maijunxian Wang, Ruisi Wang, Juyi Lin, ..., Hua XU, ..., Hokin Deng†

43rd International Conference on Machine Learning (ICML) 2026 Accepted

Summary A GrowAI project introducing the Very Big Video Reasoning (VBVR) suite, a large-scale dataset and benchmark for evaluating video models' spatiotemporal reasoning, generalization, and verifiable task-solving abilities.

My role GrowAI contributor and large-team co-author; helped frame cognitive-evaluation tasks for testing video generative models' reasoning abilities.

[Project] [arXiv]

Building Interpretable, Trustworthy Systems for Neural Signal Decoding

Cognitive Modeling Single-author research proposal

Hua XU

AAAI 2026 Undergraduate Consortium 2026 Accepted proposal

Summary Single-author Undergraduate Consortium research proposal on developing interpretable and trustworthy neural signal decoding systems.

My role Single-author undergraduate consortium proposal; formulated the problem framing and research agenda for interpretable, trustworthy neural signal decoding.

2025

IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction

Time series Completed method paper

Zhangyi Hu*, Jiemin Wu*, Hua XU*, Mingqian Liao, Ninghui Feng, Bo Gao, Songning Lai, Yutao Yue (* equal contribution)

42nd International Conference on Machine Learning (ICML) 2025 Accepted

Summary Leveraging visual pretrained masked autoencoders to address irregular multivariate time series prediction challenges by converting sparse data into time × channel image-like patches, capturing cross-channel interactions with superior accuracy and strong few-shot performance.

My role Equal-contribution core contributor; helped design and run experiments, write the paper, and participate in the rebuttal process.

[arXiv] [Code]

vNeck: Electrical Muscle Stimulation Around the Neck for Eyes-Free Target Acquisition in Virtual Reality

HCI HCI best-paper contribution

Yuchao ZHUO, Hua XU, Yucheng Liu, Duotun Wang, Jianhao Chen, Jiawei Li, Chutian Jiang, Tristan Camille Braud, Mingming Fan

International Conference on Human-Engaged Computing (ICHEC) 2025 Accepted Best Paper

Summary An innovative VR interaction system using electrical muscle stimulation around the neck for eyes-free target acquisition in virtual reality environments.

My role Responsible for the user study and data processing.