COLM 2026 ReviewsDiscussion [D]

2026-05-22 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Intermediate, quick

Summary

The COLM 2026 conference review process has generated significant discussion among submitters, with many expressing concerns over review quality. Submissions reportedly received a mixed bag of feedback, including instances of harsh, picky, and potentially AI-generated critiques. Several submitters noted reviews that were structurally identical to default generated outputs, featuring generic summaries followed by extensive, sometimes irrelevant, bulleted lists of rejections. Specific examples included rating/confidence scores like 6/3, 2/5, 6/3, 7/4, and 8 (4), 7 (3), 5 (4). This sentiment suggests COLM reviews are perceived as more stringent and less constructive than those from larger machine learning conferences, prompting questions about the value of rebuttals and the impact of perceived lazy Associate Chairs.

Key takeaway

For research scientists or AI students considering academic conference submissions, be prepared for highly critical and potentially AI-generated reviews, especially at newer venues like COLM. Your rebuttal strategy should account for the possibility of one negative review sinking an otherwise strong paper, and you should critically evaluate the constructive value of feedback. Furthermore, remember that robust "boring engineering" practices, covering monitoring, inference costs, and data pipelines, are crucial for any project's real-world viability, a factor often underestimated in pure model development.

Key insights

The quality of academic conference reviews, particularly at COLM 2026, is a significant concern due to perceived harshness and AI-generated content.

Principles

• "Boring engineering" is critical for a project's production survival beyond initial demos.
• Conference review quality can vary widely, impacting submission outcomes.

Topics

• Conference Reviews
• Academic Publishing
• AI-Generated Content
• Machine Learning Conferences
• Production ML
• Engineering Best Practices

Best for: AI Scientist, Research Scientist, AI Student

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