KDD 2025 2nd-round Review Results: How Did Your Paper Do?

[river]

KDD community stats

https://papercopilot.com/statistics/kdd-statistics/kdd-2025-statistics/

[river]

@lelecao I feel you, been there too!

[river]

I made a summary of data points from KDD 2025 1st round results:

Novelty Scores	Technical Quality Scores	Confidence Scores	Rebuttal Outcome	Final Decision	Notes
3 3 3 3 3 3	4 3 3 2 3 2	–	Addressed issues	Accepted	"Rebuttal is so difficult with all the twists and turns"
2 2 3 2 2	3 3 2 2 3	3 3 3 3 3	Submitted	Rejected	"Can I just run away?"
4 3 3 1	4 4 2 2	–	Explained issues	Rejected	"Large variance across reviewers; no score changes post-rebuttal"
3 3 3	3 3 2	–	Unsure	🟡 Unknown	"Still considering rebuttal; not sure if it's worth the effort"
3 3 3 3 3 3	3 3 3 3 3 2	–	Minor clarifications	Accepted	"Final scores unchanged but accepted after positive AC decision"
3 4 3 3 3 3	2 2 3 2 2 3	–	Clarified results	Rejected	"Novelty OK, but TQ too weak; didn't convince reviewers"
3 3 3 3 3 3	3 3 3 3 3 3	3 3 3 3 3	Submitted	Accepted	"Strong consensus; one of the smoother cases"
3 3 3	3 3 2	–	No rebuttal	Rejected	"No rebuttal submitted; borderline scores"
3 3 2 2	3 3 2 2	–	Rebuttal sent	Rejected	"Reviewers did not change their opinion"
3 3 3 3 3 3	3 3 3 3 2 2	–	Rebuttal helped	Accepted	"Accepted despite one weaker reviewer"
3 3 3 3	3 3 3 3	3 3 3 3	Rebuttal sent	🟡 Unknown	"In limbo; waiting for final decision"
3 3 3 3	2 2 2 2	–	Not convincing	Rejected	"Work deemed not ‘KDD-level’ despite rebuttal"
3 3 3 3 3 3	3 3 3 3 3 3	3 3 3 3 3	Submitted	Accepted	"Perfectly consistent reviewers; smooth acceptance"
3 3 3 2	3 3 2 2	–	Rebuttal failed	Rejected	"Low technical quality and variance led to rejection"

Note: Data sourced from community discussions on Zhihu, Reddit, and OpenReview threads. Subject to sample bias.

[lelecao]

@magicparrots

So sorry to hear that — sounds like a solid paper.

For my case,
One reviewer gave two 2s just because they didn’t see the value of improving efficiency or where it would be useful, even though that’s the whole point of many ML contributions. Another reviewer didn’t understand the paper and asked for line-by-line comments on pseudocode. That’s just disheartening.

Also noticed each review response is limited to 2500 characters. Does anyone know if we can reply in multiple stacked comments?

[Kevin Cris]

https://www.zhihu.com/question/12035973262/answers/updated
some data points from Chinese researcher community

[Hu8kKo34]

Anyone knows the likelihood of an NLP (LLM agent and its evaluation on many public datasets) work accepted to KDD, either main or applied data science track?

[Nilesh Verma]

what are the chances of acceptance in KDD feb, here is my score

Relevance: 3.5 (based on 4, 3, 4, 3, 4)
Novelty: 3.0 (based on 4, 3, 2, 3, 2)
Technical Quality: 3.0 (based on 3, 3, 3, 3, 3)
Presentation: 2.8 (based on 3, 3, 3, 2, 3)
Reproducibility: 3.0 (based on 3, 3, 3, 3, 3)
Reviewer Confidence: 3.4 (based on 3, 4, 3, 4, 3)

[Sylvia]

@Nilesh-Verma from what I hear, Novelty and TQ (combined with confidence) are two most important dimension for making the final decision. I think TQ scores are pretty good; Novelty scores are not bad either. If rebuttal can increase one of the "2"s to 3, then the chance of getting an acceptance will be even higher.

[root]

I hereby paste the historical acceptance rate of KDD research tracks

Conference	Long Paper Acceptance Rate
KDD'14	14.6% (151/1036)
KDD'15	19.5% (160/819)
KDD'16	13.7% (142/1115)
KDD'17	17.4% (130/748)
KDD'18	18.4% (181/983) (107 orals and 74 posters)
KDD'19	14.2% (170/1200) (110 orals and 60 posters)
KDD'20	16.9% (216/1279)
KDD'22	15.0% (254/1695)
KDD'23	22.1% (313/1416)
KDD'24	20.0% (411/2046)

Note that KDD'24 accepted 151 ADS track papers from 738 submissions!

[Sylvia]

The KDD PC just opened the comment phase until Apr 18 (AoE). You can respond to reviewer follow-ups or raise concerns to AC/SAC via the Official Comment button.

️ A few don’ts:

• No URLs — they’ll auto-delete your comment.
• No bypassing rebuttal limits — don’t treat comments as extra rebuttal space.
• Don’t badger reviewers — 1 ping is enough.
• Stay respectful — tone matters.

Good luck everyone