AI conference ICLR is drowning in abstracts, with roughly 50,000 submissions before the deadline
ICLR 2027 received roughly 50,000 abstracts before its deadline, far above ICLR 2026's 19,500 valid submissions, straining peer review.
ICLR 2027 has collected about 50,000 abstracts roughly a week before the deadline, compared with around 19,500 valid submissions at ICLR 2026. The final count is expected to be lower as authors withdraw papers accepted at NeurIPS, but will remain far above last year. The surge is attributed to AI hype, corporate research incentives tied to publications, and AI tools dramatically speeding up paper writing. ICLR 2026 already suffered from low-quality AI-generated submissions with fabricated citations and reviewers using AI to cope, eroding trust in peer review.
- ICLR 2027 abstracts (~50,000) far exceed ICLR 2026's ~19,500 valid submissions.
- Authors hedge between ICLR and NeurIPS, so many abstracts will likely be withdrawn.
- A NeurIPS analysis found authors heavily used AI to write their submissions.
- ICLR 2026 saw fabricated citations in AI-generated papers and AI-assisted reviews.
- Corporate pay sometimes tied to publication records, fueling submission volume.
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ICLR 2027 has already pulled in about 50,000 abstracts, and the deadline isn't until next week. ICLR 2026 saw around 19,500 valid submissions. Some abstracts likely come from authors hedging their bets, waiting on NeurIPS results and planning to withdraw if accepted there. So the final count will be lower, but still far above last year.
The broader AI hype is likely fueling this flood, as is corporate research spending, where pay is sometimes tied to publication records. But the biggest factor is probably that AI makes it much faster to crank out papers. A NeurIPS analysis found that authors used AI heavily to write their submissions.
ICLR 2026 already struggled with low-quality AI-generated submissions and reviews that eroded trust in peer review. Authors submitted AI-generated papers packed with fabricated citations, and reviewers turned to AI just to keep up with the volume. With even more papers flooding in this year, expect those complaints to get louder.
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