Chenxi Zhou

Chenxi Zhou has 2 papers in our library, in 2026, most of them on Language models & pretraining, Efficient models & compression; often with Bohan Yu, Jiang Li, Jinyu Ye.

2 papers in our library · 2026

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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).

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026 · 1 citation)
The paper asks why weight-only post-training quantization (PTQ) of LLMs behaves acceptably at 4-bit but breaks down almost completely at 2-bit.
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026)
Scaling laws for quantized LLMs usually predict only aggregate performance from model size and bit-width. They treat GPTQ-style hyperparameters as fixed and ignore that different kinds of knowledge may degrade differently. This paper builds a multiplicative power-law model of post-training quantization (PTQ) that…

6 co-authors with a page, the most papers together first.

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

Updated 2 November 2026