Bei Yu

Bei Yu has 3 papers in our library, in 2026, most of them on Evaluation & benchmarks; often with Haoru Tan, Hui-Ling Zhen, Jiaya Jia.

3 papers in our library · 2026

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library, with the number of works on each.

The most cited first (OpenAlex and our library).

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026 · 1 citation)
Most inference compute in large language models goes to the feed-forward (FFN) blocks. Converting a dense model into a Mixture-of-Experts (MoE) can cut that cost, but earlier conversion methods needed continual training on billions of tokens. Pei et al. instead restructure FFNs after training, in a single analytical…
PROBE: PROcess-Based BEnchmark for Hallucination Detection (2026)
LLM-as-a-judge setups usually ask a model for a single verdict on whether an output stays faithful to its source.
TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026)
Reinforcement learning for LLM reasoning usually depends on a single correctness signal at the end of a solution.

12 of 15 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated 2 November 2026