Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. - Zixuan Wang 029; Stanley Wei; Daniel Hsu 001; Jason D. Lee (ICML, 2024)

Learning and Transferring Sparse Contextual Bigrams with Linear Transformers. - Yunwei Ren; Zixuan Wang 029; Jason D. Lee (NeurIPS, 2024)

Learning Compositional Functions with Transformers from Easy-to-Hard Data. - Zixuan Wang 029; Eshaan Nichani; Alberto Bietti; Alex Damian; Daniel Hsu 001; Jason D. Lee; Denny Wu (COLT, 2025)

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought. - Jianhao Huang; Zixuan Wang 029; Jason D. Lee (ICLR, 2025)

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? - Zihan Zheng; Zerui Cheng; Zeyu Shen; Shang Zhou; Kaiyuan Liu; Hansen He; Dongruixuan Li; Stanley Wei; Hangyi Hao; Jianzhu Yao; Peiyao Sheng; Zixuan Wang 029; et al. (NeurIPS, 2025)

What Makes a Reward Model a Good Teacher? An Optimization Perspective. - Noam Razin; Zixuan Wang 029; Hubert Strauss; Stanley Wei; Jason D. Lee; Sanjeev Arora (CoRR, 2025)

The Power of Power Law: Asymmetry Enables Compositional Reasoning. - Zixuan Wang 029; Xingyu Dang; Jason D. Lee; Kaifeng Lyu (CoRR, 2026)