Author

Jiachun Li

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#artificial intelligence Jun 2026

Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation

This work introduces Bayesian in-context experimenters: transformer policies trained to imitate a Bayesian posterior Neyman teacher, and proves this amortized policy can be learned via empirical risk minimization using supervised pretraining.

Jiachun Li, David Simchi-Levi · arXiv.org · 0 citations

#artificial intelligence Apr 2026

LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency

A score-whitening method that equalizes local Fisher information and restores stable inference at the optimal sample-complexity scale is introduced that provides a principled framework for uncertainty quantification in LLM evaluation and more broadly for inference on low-rank structures from pairwise data.

Jiachun Li, David Simchi-Levi, W. Sun · arXiv.org · 2 citations

Jul 2026

An Isotropy-Preserving Spectral Cap for Muon: Theory and Three Case Studies

A unified framework built on a single idealizing assumption -- exact scale invariance of the loss under weight rescaling, which holds approximately in normalization-heavy networks is proposed.

Jiachun Li · arXiv.org · 0 citations