ACM MM 2026 review discussion [D]

2026-05-28 · Source: Machine Learning · Field: Science & Research — Research Methodology & Innovation, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The ACM MM 2026 conference review discussion highlights significant concerns among submitters regarding the rebuttal process and review quality. Participants expressed confusion over a short rebuttal period, noting an AC email indicating dates between the 28th and 4th, with June 4th as the website deadline. A major issue raised was the prevalence of Large Language Model (LLM)-generated reviews, described as "pure garbage" and lacking substantive feedback. Additionally, there was widespread confusion about the main track's rating scale, specifically whether it was 1-5 with 5 as maximum, and why a 4/5 score translated to "Weak accept," contrasting with ICML's previous year where 4 was an "accept." Submitters also shared their scores like "3,2,3,3,2" and "4,4,3,3,3" to gauge acceptance chances.

Key takeaway

For research scientists submitting to conferences like ACM MM 2026, carefully scrutinize review feedback for signs of LLM generation, which often provides unhelpful, generic comments. Be proactive in confirming rebuttal deadlines directly with organizers, as discrepancies between emails and websites can shorten critical response times. Understand the specific conference's rating scale nuances to accurately interpret scores and assess your paper's acceptance probability, rather than relying on assumptions from other venues.

Key insights

ACM MM 2026 submitters face short rebuttal windows, LLM-generated reviews, and unclear rating scales, complicating paper acceptance.

Principles

• Conference review processes vary significantly.
• LLM-generated reviews lack depth.
• Rating scales require clear communication.

In practice

• Verify rebuttal deadlines immediately.
• Scrutinize reviews for LLM indicators.
• Clarify rating scale interpretations.

Topics

• ACM MM 2026
• Conference Peer Review
• LLM-Generated Reviews
• Rebuttal Process
• Academic Publishing
• Review Rating Scales

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.