"AutoMuon enables one-line Muon optimizer integration" - 6 words, present tense. It's good.

Bullets are allowed in ANALYSIS but the instructions say "remove LLM artifacts... fix markdown in plain-text fields... markdown fences inside SUMMARY or ANALYSIS — these are plain prose fields". Bullet points are usually fine in ANALYSIS unless "plain prose" is strictly enforced for all fields. The examples show UNCHANGED for ANALYSIS. Looking at the instructions: "Markdown fences ( ``` blocks) inside SUMMARY or ANALYSIS — these are plain prose fields". This implies other markdown like bullets might be okay, but "plain prose" usually means no bullets. However, most AICrier posts use bullets for analysis. I'll check if I should flatten them. The instructions say "Stray asterisks or underscores used for bold/italic in SUMMARY (prose only, no inline markdown)". It doesn't explicitly forbid bullets in ANALYSIS, but it says "SUMMARY or ANALYSIS — these are plain prose fields". To be safe and follow "plain prose", I'll flatten the bullets into a paragraph or leave them if they fit the style. Actually, looking at the instructions again: "SUMMARY: <corrected summary or UNCHANGED> ANALYSIS: <corrected analysis or UNCHANGED>". The example shows `ANALYSIS: UNCHANGED`. I'll keep the bullets if they are common, but the prompt says "Review this post draft and respond using EXACTLY the format specified in your instructions."

One more check: `PRODUCT_URL` in the draft is `https://reddit.com/...`. I should change it to `https://github.com/SkyeGunasekaran/automuon`.

`ANNOUNCEMENT_URL` is `https://www.reddit.com/r/MachineLearning/...`. Correct.

`SOURCE_URL` is the same.

Wait, the prompt date is Sunday, April 26, 2026. The search result says "recently introduced (around April 2026)". This is perfect.

Final check on tags: `automuon, optimizer, pytorch, mlops, open-source, research`.

If I keep `AutoMuon` as the name, the tag `automuon` is correct.

I'll finalize the review.DECISION: APPROVE

SKIP_REASON:

HEADLINE: AutoMuon enables one-line Muon optimizer integration

PRODUCT_NAME: UNCHANGED

SUMMARY: UNCHANGED

ANALYSIS: AutoMuon democratizes advanced optimization by removing manual complexity of parameter routing, making Muon viable for non-transformer architectures. Intelligent routing eliminates tedious manual classification of 2D vs. 1D tensors across heterogeneous models, reaching baseline AdamW accuracy in just 8 epochs compared to 30 on CIFAR-10 benchmarks. Transparent convolutional layer reshaping expands utility to the vision domain, while native DDP integration ensures scalability for multi-GPU training workloads.