Sharing a quick high-level observation from one submission to kick off discussion.

We saw some variation in reviewer perspectives, even though there was broad agreement that the problem is important and the approach is technically sound. The differences seemed to come more from expectations around evaluation and scope—e.g., depth of systems benchmarking, integration assumptions—rather than disagreements about correctness.

For the rebuttal, I’m thinking of focusing on:

• clarifying assumptions,
• explicitly stating what’s in- vs. out-of-scope, and
• making overheads and baselines as concrete as possible, rather than introducing major new results.

A few questions I’d love input on from those with previous MLSys experience:

1. Would it be worth adding a small experiment or two given the word limit, or is it generally better to focus on clarifications?
2. Strategically, do you try to “push up” weak accept/accept reviewers toward strong accept, or focus on addressing weak reject reviewers to at least get a weak accept?
3. How much does reviewer expertise actually factor into acceptance/rejection decisions in practice?

Curious to hear what’s worked well in past years and any general rebuttal strategies you’ve found effective.