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

[Hsi Ping Li]

@river Hi river,

Excuse me, do you know if these scores are the final scores after the rebuttal? Really appreciate it if you could provide more information about this

[Hsi Ping Li]

@Nilesh-Verma Hi Nilesh, I am sure the scores of your paper are higher than those of most authors. Congs. Besides, did your reviewers increase their ratings for your paper in the rebuttal process?

[river]

@Hsi-Ping-Li

This the best effort scores, meaning I take the latest available scores reported in the community. If they are updated by the authors after rebuttal, then I take that, otherwise I would assume the scores did not change.

For the data points with accept/reject outcome, I think all of them are post-rebuttal scores.

[Hsi Ping Li]

@magicparrots

Hi magicparrots!

did the reviewers raise their scores for your paper after the rebuttal process?
I also submitted a paper about GNN, and only one reviewer out of five raised 1 score for my paper

[Hsi Ping Li]

@river Many thanks for your details!

[root]

Stats from official email:

The Research Track of KDD 2025 (February Cycle) received 1988 submissions, with an overall acceptance rate of ~18.4%. All submissions received at least three reviews, while most had four or five. Area Chairs provided meta-reviews and preliminary recommendations, which were deliberated further by the Senior Area Chairs and decided on by the Program Chairs.

...

A submission rejected from the Research Track may not be resubmitted within 12 months to the KDD Research Track (i.e., the earliest resubmission date of your paper to the KDD research track is February 2026).

[Joanne]

Thanks for the information. Especially the resubmission restriction. Something to watch out for when planning next steps.

[Joanne]

KDD 2025 (February Cycle) – What the Score Patterns Reveal

After combing through 22 self-reported results, three consistent patterns jump out:

• All-3’s are not lethal. Several papers with a flat 3-3 profile survived because nobody down-voted hard and the Area Chair (AC) was on their side.
• 4–2 vs 3–3 is still a coin-flip. A spiky 4–2 pair can trump steady 3–3s, yet clean consistency sometimes wins when the AC trusts uniform support.
• Reviewer kindness matters. A single upgrade (e.g., Technical 3 → 4) in the last round carried borderline submissions over the line.

---

Who Actually Got In? – Mini Score Sheet

Alias	Final Mean (N / T)	Earlier Lows	Verdict
author 1 #1	3.6 / 4.0	early 3-3-4 mix	Accept
author 1 #2	3.6 / 3.4	weaker T	Accept
author 2	≈ 3.2 / 2.8	one reviewer gave 2 / 2	Accept — “kind-hearted AC”
author 3	3.0 / 3.0	flat all-3’s	Accept
author 4	3.0 / 3.0	two negative votes (2 / 2)	Accept
author 5	3.4 / 4.0	T started 3-3-2-2-2	Accept — generous reviewer bumped T to 4

---

Messages from this Small Sample

1. ≈ 3.0 averages can pass — the AC’s veto (positive or negative) is the real gatekeeper.
2. One low score plus a confident critique can still sink you — numbers alone aren’t everything.
3. Polite, point-by-point rebuttals can move scores, though not as often as we’d like.

How's your scores? We will make a new pattern after you share with us your.

[cocktailfreedom]

@Joanne said in KDD 2025 2nd-round Review Results: How Did Your Paper Do?:

KDD 2025 (February Cycle) – What the Score Patterns Reveal

After combing through 22 self-reported results, three consistent patterns jump out:

• All-3’s are not lethal. Several papers with a flat 3-3 profile survived because nobody down-voted hard and the Area Chair (AC) was on their side.
• 4–2 vs 3–3 is still a coin-flip. A spiky 4–2 pair can trump steady 3–3s, yet clean consistency sometimes wins when the AC trusts uniform support.
• Reviewer kindness matters. A single upgrade (e.g., Technical 3 → 4) in the last round carried borderline submissions over the line.

---

Who Actually Got In? – Mini Score Sheet

Alias	Final Mean (N / T)	Earlier Lows	Verdict
author 1 #1	3.6 / 4.0	early 3-3-4 mix	Accept
author 1 #2	3.6 / 3.4	weaker T	Accept
author 2	≈ 3.2 / 2.8	one reviewer gave 2 / 2	Accept — “kind-hearted AC”
author 3	3.0 / 3.0	flat all-3’s	Accept
author 4	3.0 / 3.0	two negative votes (2 / 2)	Accept
author 5	3.4 / 4.0	T started 3-3-2-2-2	Accept — generous reviewer bumped T to 4

---

Messages from this Small Sample

1. ≈ 3.0 averages can pass — the AC’s veto (positive or negative) is the real gatekeeper.
2. One low score plus a confident critique can still sink you — numbers alone aren’t everything.
3. Polite, point-by-point rebuttals can move scores, though not as often as we’d like.

How's your scores? We will make a new pattern after you share with us your.

Thanks for sharing! mine got rejected though -- mean T score 2.5-ish

[Joanne]

@cocktailfreedom Thanks for sharing that and sorry to hear about the rejection. A 2.5 mean T score definitely stings, but it says nothing about your potential or the value of your work long term. Peer review can be noisy, biased, or just not aligned with where your idea fits best.
Let me share Saining Xie's comment “I wouldn’t call conferences a lottery, but a bit of perseverance does go a long way.”