mlsys-review-process
Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage means for review strategy.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLSys Review Process Use this to model what happens to a Conference on Machine Learning and Systems submission between upload and decision. Mechanics below are 2026-cycle anchors (verified 2026-07-08); the venue is young and still redesigns its process — 2026 alone added an entire track — so reopen the current CFP and OpenReview group before strategic decisions. ## The pipeline (2026 anchors) - Submission via OpenReview (`MLSys.org/2026/Conference` group) by October 30, 2025. - Double-blind review; arXiv posting allowed in parallel. - Reviews released January 12, 2026; author responses due January 16; notifications January 25-26. There is no long discussion phase to rescue a paper — the response is a single, short shot (see `mlsys-author-response`). - Accepted papers publish on proceedings.mlsys.org; artifact evaluation follows as a separate, optional, badge-awarding stage (March 8 - April 8 in 2026). ## Who reviews here — the two-culture pool MLSys program committees deliberately mix ML researchers with systems, architecture, and compiler people. The same paper is read through two different quality lenses: | Dimension | ML-culture reviewer asks | Systems-culture reviewer asks |
- The pipeline (2026 anchors)
- Who reviews here — the two-culture pool
- What decisions actually turn on
- Reading a review packet
- Reading scores and reviewer signals
- Confidentiality and conduct
- After the decision
- Cycle-volatility warnings
- Output format
What does the mlsys-review-process skill do?
Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage means for review strategy.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.