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    R
    love the insights about "What Counts as Good Stitching?"!
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    rootR
    This Year’s Community Observations Reviewer Silence: Most authors receive little to no real engagement — sometimes only an MA. Score Disappearance: If a reviewer updates their rating/final justification, their score vanishes from the author console until decisions. Community Frustration: Many feel the “discussion period” is a one-way street; reviewers rarely debate or clarify further. Randomness: Even with identical scores, some papers get in, others don’t. “AC lottery” and “reviewer lottery” are real! Field Differences: Some subfields have lower average scores; a 4.0 may be top-20% in a tough area, borderline in others. Exhaustive Table: Score Scenarios & Acceptance Hope Below is a table constructed from real examples in the community (Zhihu, Reddit, etc.). Case Initial Scores (C=Confidence) Actions During Rebuttal What Happened After Community Analysis Acceptance Hope A 5,5,4,3 One 4 promised to increase; 3 is silent Avg. visible 4.67 or 4.0 (if one vanishes) 4+ avg with one “up” and no strong negative Very good chance B 5,4,4,2 2 was highest confidence; author tried to convince; 4 promised up Score vanishes after “up”, avg. jumps if visible If both “up,” strong chance. If 2 stays, borderline; depends on AC C 4,4,4,4 All 4s; only MA or silence Avg 4.0 No reject, but no clear “champion.” Depends on AC and field bar 50–75% (“bubble”) D 5,5,3,2 (C: 3,3,5,3) Tried to “fix” the 2; 3 was picky Only MA; avg 3.75/4.0 Negative champion can kill it if not convinced; AC matters Risky but possible if AC sympathetic E 3,3,4,5 4 promised “up”; 3 did MA Avg jumps (3.75–4.0) If “up” confirmed, likely to be accepted, esp. if 3 is no longer counted Good odds if “up” happens F 3,4,4,4,5 4 said updated; 5 silent Avg 4.2 Most positive, one “update” is good Likely accept if no major issues G 3,3,3,4 Only one 4, rest 3s, some silence Avg 3.25 Borderline or below, need at least one “up” Unlikely unless field has very low average H 4,4,3,3 4s MA, 3s silent Avg 3.5–4.0 Borderline, need at least one 3 to up True “bubble”—AC can go either way I 5,4,3,3 (C: 2,1,4,4) Both 3s high confidence; all silent Avg 3.75 Weak, but not hopeless. AC might tip if rebuttal strong. 30–50% J 5,5,5,4,3 4 says will up, 3 silent Avg >4 Excellent if 4 moves up, still good if not K 4,4,3,2 All silent Avg 3.25 Very hard; needs miracle up-vote from 2 or 3 Unlikely L 5,4,3,2; up to 5,5,3,3 After rebuttal all up by 1 Avg now 4 If real, strong, but only if up-votes stick M 3,5,5,2 Tried hard to move 2, 5 says up If avg now 4+, possible Must move 2 or AC must ignore 40% N 4,4,4,3 All MA, no comments Avg 3.75 Bubble, depends on AC or field O 2,3,5,5 3/2 not responding, 5/5 happy Avg 3.75 If no “up,” borderline; depends on AC and field 30–60% P 3,4,5,5 Some up, some down Avg 4.25 Good if up, depends if 3/4 “negative champion” Q 2,2,2,2 Submitted rebuttal anyway Avg 2 “No hope” zone R 4,3,5,5 3 not convinced, 5/5 positive Avg 4.25 Good odds, but 3 can kill if AC is negative S 3,3,4,4 (C: 4,4,3,3), all said “up” All promise up, scores vanish Should be 4/4/4/4+ Very strong, likely to be accepted T 4,4,4,5 MA only, no real engagement Avg 4.25 No negative champion, likely safe U 5,4,4,2; 2 up to 3, 4 to 5 Scores vanish, avg >4 Strong, very likely accept V 5,4,4,3; MA from 4s, 3 silent Avg 4.0 Borderline, need at least one “up” W 5,4,3,2; 2 still visible after MA 2 unchanged Low chance unless AC overrules X 3,4,4,4 3 says “thanks, still weak” Avg 3.75 Need 3 to up, else risky Y 3,3,3,4,4 All 3s, one 4 Avg 3.4 Very difficult Z 5,5,4,2 2 is negative champion Avg 4 On the line; needs AC intervention What the Community Says Silence ≠ Rejection: Most reviewers do not reply, especially to borderline or mid-range scores. Don’t panic! MA Means Finalized: Once you see “Mandatory Acknowledgement,” you can’t see the score if they updated it, but they may have changed it up or down. Upvotes = Hope: Any reviewer explicitly saying “I am raising my score” is good; if the score vanishes, assume they did. Downvotes Happen: Occasionally, a reviewer will lower after rebuttal; this is rare but possible if the rebuttal is weak or reveals flaws. “Negative Champion” Danger: A single reviewer (usually the lowest) with high confidence can block a paper if not convinced. Always try to address their points specifically. Area Chairs (AC) Role ACs often have leeway in borderline cases, especially if rebuttal is strong or all concerns are addressed except one. If all reviewers are “neutral” (3s and 4s), and no strong negative, ACs may push for acceptance, especially if field is tough. Community advice: if you have real engagement from at least one reviewer, and no strong rejections, odds are decent. Finally Here are the main takeaways from recent statistics (see screenshots): [image: 1754406036456-screenshot-2025-08-05-at-16.59.41.jpg] Acceptance Rate: NeurIPS acceptance rate has hovered around 25% for the last five years (see Paper Copilot NeurIPS 2025 statistics). Score Distribution: Most “active” (not withdrawn) papers have initial means in the 3.0–4.2 range. Community feedback is consistent with these tools: avg. 4+ is usually "safe", 3.5–4.0 is a coin toss, <3.5 is very unlikely. Confidences help: 4s and 5s from high-confidence reviewers are
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    C
    Thanks for clarifying this, very helpful!
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    H
    Any one know whet does the following message mean in Openreview? kinda confused. [image: 1753132273154-screenshot-2025-07-21-at-23.08.45.png]
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    JoanneJ
    Yeah. Can't wait to see how AAAI 2026 First AI-Assisted Peer Review performs.
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    JoanneJ
    This is not the first time to have "F" word in journal paper, but it's on the most impactful journal. One of the ridiculous paper published was on International Journal of Advanced Computer Technology, originally created by two computer scientists in 2005 as a joke response to spammy academic invitations, with the title: <Get me off Your Fucking Mailing List>. [image: 1748104001215-c78069b7-5d54-427a-8d57-11b24233374d-image.png] [image: 1748103841726-bfa55487-5ecb-428c-9838-b91ab33ef101-image.png] [image: 1748103873929-87fa34a9-e26d-49a3-9781-9c2cd93b654f-image.png] Then, there is an other paper published by Vamplew, Peter tilted: "Get me off Your Fucking Mailing List." in Зборник Матице српске за друштвене науке 154 (2016), abut this. Vamplew has this written in the abstract: "A paper titled “Get me off your fcking mailing list” has been accepted by the International Journal of Advanced Computer Technology. But, as Joseph Stromberg reports for Vox, there’s more going on here than just a hilariously missing-in-action peer-review system – it highlights the bigger problem of predatory journals, which try to get young academics pay to have their work published, and shows just how shonky they are. Despite how fancy the journal sounds, the International Journal of Advanced Computer Technology is actually an open-access publication that spams thousands of scientists every day with the offer of publishing their work – for a price, of course. Back in 2005, US computer scientists David Mazières and Eddie Kohler created this 10-page paper as a joke response they could send to annoying and unwanted conference invitations. As well as the seven-word headline being repeated over and over again, the paper also contained some very helpful flow charts and graphs, [....] [See Figure 1 above!] The PDF went pretty viral in academic circles, and then recently an Australian scientist named Peter Vamplew sent it off to the pain-in-the-ass International Journal of Advanced Computer Technology in the hope that the editors would open it, read it and take him off their fcking list. Instead, Scholarly Open Access reports that they took it as a real submission and said they’d publish it for $150. Apparently the journal even sent the paper to an anonymous reviewer who said it was “excellent”. As Stromberg writes for Vox: “This incident is pretty hilarious. But it’s a sign of a bigger problem in science publishing. This journal is one of many online-only, forprofit operations that take advantage of inexperienced researchers under pressure to publish their work in any outlet that seems superficially legitimate. They’re very different from respected, rigorous journals like Science and Nature that publish much of the research you read about in the news. Most troublingly, the predatory journals don’t conduct peer-review – the process where other scientists in the field evaluate a paper before it’s published.” Not only that, but in this instance the journal didn’t even seem to care that the scientist who submitted it wasn’t actually the one who wrote the article. This isn’t the first time these predatory journals have been caught out, Stromberg reports, but unfortunately it shows that the problem doesn’t seem to be going anywhere anytime soon. Read Stromberg’s excellent full story on the paper and predatory journals over at Vox. And next time we get spammed by unwanted emails, we know what we’ll be sending back."
  • 1 Votes
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    JoanneJ
    I also heard some negative ones. [image: 1747115726301-9ec54026-13df-43c4-90cf-d0c1b5e651f2-image.png] [image: 1747115784562-df6bcec1-bf5b-446d-a626-2037490ea36f-image.png] but it seems iccv doesn't allow the usage of chatgpt etc in review process: [image: 1747115959677-6e4057c2-55e9-45c7-b1e7-4b6ff2645928-image.png] [image: 1747115805501-2b07b416-a61c-48b9-9039-c45b7322dbe3-image.png] [image: 1747115830768-cd7ee90a-e148-4a88-9045-ed712cf087d3-image.png] Let's hear the reviewer side of story, but didn't say why: [image: 1747116023274-8365cfc3-cceb-4c42-9b81-27623bd26a7e-image.png]
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    JoanneJ
    Hey @magicparrots that sounds like a pretty serious slip if true I would love to hear more if you have a source or screenshot though.
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    J
    @Joanne Yes, I think you're exactly correct -- experience matters. I've seen people on social media saying that "some PhD students are very talented nowadays". But no matter how talented they are, they still lack experience. The most important thing for an AC is to make decisions/recommendations about submissions based on their experience, even when the reviews are "noisy". I don't think PhD students can do this well, even if they are exceptionally talented. But for now, it seems that we can't avoid this, so we just have to be mentally prepared for it. Things may get worse.
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    rootR
    Here's a glimpse of some truly remarkable work recognized this year: Outstanding Papers: Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction Vaishnavh Nagarajan, Chen Wu, Charles Ding, Aditi Raghunathan The Value of Prediction in Identifying the Worst-Off Unai Fischer Abaigar, Christoph Kern, Juan Perdomo Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, Sitan Chen Score Matching with Missing Data Josh Givens, Song Liu, Henry Reeve CollabLLM: From Passive Responders to Active Collaborators Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao Conformal Prediction as Bayesian Quadrature Jake Snell, Thomas Griffiths Outstanding Position Papers: AI Safety should prioritize the Future of Work Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards Jaeho Kim, Yunseok Lee, Seulki Lee See all awards and details here
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    JoanneJ
    ICLR 2026 will be hosted in Brazil. [image: 1748105858945-710873cc-02f7-4cd3-a955-4146af74d285-image.png] What's this one in Portugal? [image: 1748105973493-c47e8394-99cf-4e5d-bdae-022a01292d2f-image.png]
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