From 8c4792e2b15bc473944b93a62d717b8e237d17e2 Mon Sep 17 00:00:00 2001
From: Yuma Ichikawa <58498114+Yuma-Ichikawa@users.noreply.github.com>
Date: Mon, 20 Apr 2026 16:38:54 +0900
Subject: [PATCH] Add OneComp/QEP to Quantization reading list

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 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
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@@ -27,6 +27,9 @@ This is a reading list of papers/videos/repos I've personally found useful as I
 * [Epilogue Visitor Tree](https://dl.acm.org/doi/10.1145/3620666.3651369): Fuse custom epilogues by adding more epilogues to the same class (visitor design pattern) and represent the whole epilogue as a tree
 
 ## Quantization
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+- [OneComp: One-Line Revolution for Generative AI Model Compression](https://arxiv.org/abs/2603.28845) — A resource-adaptive PTQ pipeline integrating progressive (layer→block→global) refinement, ILP-based mixed-precision assignment, and SpinQuant/OstQuant-style rotation preprocessing. [Code](https://github.com/FujitsuResearch/OneCompression).
+- [Quantization Error Propagation (NeurIPS 2025)](https://openreview.net/forum?id=a3l3K9khbL) — Propagates quantization residuals to downstream layers.
 * [A White Paper on Neural Network Quantization](https://arxiv.org/abs/2106.08295): Start here this is will give you the foundation to quickly skim all the other papers
 * [LLM.int8](https://arxiv.org/abs/2208.07339): All of Dettmers papers are great but this is a natural intro
 * [FP8 formats for deep learning](https://arxiv.org/abs/2209.05433): For a first hand look of how new number formats come about