CSPaper: peer review sidekick

Shocking Cases, Reviewer Rants, Score Dramas, and the True Face of CV Top-tier Peer Review! “Just got a small heart attack reading the title.” — u/Intrepid-Essay-3283, Reddit [image: giphy.gif] Introduction: ICCV 2025 — Not Just Another Year ICCV 2025 might have broken submission records (11,239 papers! 🤯), but what really set this year apart was the open outpouring of review experiences, drama, and critique across communities like Zhihu and Reddit. If you think peer review is just technical feedback, think again. This year, it was a social experiment in bias, randomness, AI-detection accusations, and — sometimes — rare acts of fairness. Below, we dissect dozens of real cases reported by the community. Expect everything: miracle accepts, heartbreak rejections, reviewer bias, AC heroics, AI accusations, desk rejects, and score manipulation. Plus, we bring you the ultimate summary table — all real, all raw. The Hall of Fame: ICCV 2025 Real Review Cases Here’s a complete table of every community case reported above. Each row is a real story. Find your favorite drama! # Initial Score Final Score Rebuttal Effect Decision Reviewer/AC Notes / Notable Points Source/Comment 1 4/4/2 5/4/4 +1, +2 Accept AC sided with authors after strong rebuttal Reddit, ElPelana 2 5/4/4 6/5/4 +1, +1 Reject Meta-review agreed novelty, but blamed single baseline & "misleading" boldface Reddit, Sufficient_Ad_4885 3 5/4/4 5/4/4 None Reject Several strong scores, still rejected Reddit, kjunhot 4 5/5/3 6/5/4 +1, +2 Accept "Should be good" - optimism confirmed! Reddit, Friendly-Angle-5367 5 4/4/4 4/4/4 None Accept "Accept with scores of 4/4/4/4 lol" Reddit, ParticularWork8424 6 5/5/4 6/5/4 +1 Accept No info on spotlight/talk/poster Reddit, Friendly-Angle-5367 7 4/3/2 4/3/3 +1 Accept AC "saved" the paper! Reddit, megaton00 8 5/5/4 6/5/4 +1 Accept (same as #6, poster/talk unknown) Reddit, Virtual_Plum121 9 5/3/2 4/4/2 mixed Reject Rebuttal didn't save it, "incrementality" issue Reddit, realogog 10 5/4/3 - - Accept Community optimism for "5-4-3 is achievable" Reddit, felolorocher 11 4/4/2 4/4/3 +1 Accept AC fought for the paper, luck matters! Reddit, Few_Refrigerator8308 12 4/3/4 4/4/5 +1 Accept Lucky with AC Reddit, Ok-Internet-196 13 5/3/3 4/3/3 -1 (from 5 to 4) Reject Reviewer simply wrote "I read the rebuttals and updated my score." Reddit, chethankodase 14 5/4/1 6/6/1 +1/+2 Reject "The reviewer had a strong personal bias, but the ACs were not convinced" Reddit, ted91512 15 5/3/3 6/5/4 +1/+2 Accept "Accepted, happy ending" Reddit, ridingabuffalo58 16 6/5/4 6/6/4 +1 Accept "Accepted but not sure if poster/oral" Reddit, InstantBuffoonery 17 6/3/2 - None Reject "Strong accept signals" still not enough Reddit, impatiens-capensis 18 5/5/2 5/5/3 +1 Accept "Reject was against the principle of our work" Reddit, SantaSoul 19 6/4/4 6/6/4 +2 Accept Community support for strong scores Reddit, curious_mortal 20 4/4/2 6/4/2 +2 Accept AC considered report about reviewer bias Reddit, DuranRafid 21 3/4/6 3/4/6 None Reject BR reviewer didn't submit final, AC rejected Reddit, Fluff269 22 355 555 +2 Accept "Any chance for oral?" Reddit, Beginning-Youth-6369 23 5/3/2 - - TBD "Had a good rebuttal, let's see!" Reddit, temporal_guy 24 4/3/4 - - TBD "Waiting for good results!" Reddit, Ok-Internet-196 25 5/5/4 5/5/4 None Accept "555 we fn did it boys" Reddit, lifex_ 26 633 554 - Accept "Here we go Hawaii♡" Reddit, DriveOdd5983 27 554 555 +1 Accept "Many thanks to AC" Reddit, GuessAIDoesTheTrick 28 345 545 +2 Accept "My first Accept!" Reddit, Fantastic_Bedroom170 29 4/4/2 232 -2, -2 Reject "Reviewers praised the paper, but still rejected" Reddit, upthread 30 5/4/4 5/4/4 None Reject "Another 5/4/4 reject here!" Reddit, kjunhot 31 432 432 None TBD "432 with hope" Zhihu, 泡泡鱼 32 444 444 None Accept "3 borderline accepts, got in!" Zhihu, 小月 33 553 555 +2 Accept "5-score reviewer roasted the 3-score reviewer" Zhihu, Ealice 34 554 555 +1 Accept "Highlight downgraded to poster, but happy" Zhihu, Frank 35 135 245 +1/+2 Reject "Met a 'bad guy' reviewer" Zhihu, Frank 36 235 445 +2 Accept "Congrats co-authors!" Zhihu, Frank 37 432 432 None Accept "AC appreciated explanation, saved the paper" Zhihu, Feng Qiao 38 442 543 +1/+1 Accept "After all, got in!" Zhihu, 结弦 39 441 441 None TBD "One reviewer 'writing randomly'" Zhihu, ppphhhttt 40 4/4/3/2 - - TBD "Asked to use more datasets for generalization" Zhihu, 随机 41 446 (443) - - TBD "Everyone changed scores last two days" Zhihu, 877129391241 42 553 553 None Accept "Thanks AC for acceptance" Zhihu, Ealice 43 4/4/3/2 - - Accept "First-time submission, fair attack points" Zhihu, 张读白 44 4/4/4 4/4/4 None Accept "Confident, hoping for luck" Zhihu, hellobug 45 5541 - - TBD "Accused of copying concurrent work" Zhihu, 凪·云抹烟霞 46 554 555 +1 Accept "Poster, but AC downgraded highlight" Zhihu, Frank 47 6/3/2 - None Reject High initial, still rejected Reddit, impatiens-capensis 48 432 432 None Accept "Average final 4, some hope" Zhihu, 泡泡鱼 49 563 564 +1 Accept "Grateful to AC!" Zhihu, 夏影 50 6/5/4 6/6/4 +1 Accept "Accepted, not sure if poster or oral" Reddit, InstantBuffoonery NOTE: This is NOT an exhaustive list of all ICCV 2025 papers, but every real individual case reported in the Zhihu and Reddit community discussions included above. Many entries were “update pending” at posting — when the author didn’t share the final result, marked as TBD. Many papers changed hands between accept/reject on details like one reviewer not updating, AC/Meta reviewer overrides, “bad guy”/mean reviewers, and luck with batch cutoff. 🧠 ICCV 2025 Review Insights: What Did We Learn? 1. Luck Matters — Sometimes More Than Merit Multiple papers with 5/5/3 or even 6/5/4 were rejected. Others with one weak reject (2) got in — sometimes only because the AC “fought for it.” "Getting lucky with the reviewers is almost as important as the quality of the paper itself." (Reddit) 2. Reviewer Quality Is All Over the Place Dozens reported short, generic, or careless reviews — sometimes 1-2 lines with major negative impact. Multiple people accused reviewers of being AI-generated (GPT/Claude/etc.) — several ran AI detectors and reported >90% “AI-written.” Desk rejects were sometimes triggered by reviewer irresponsibility (ICCV officially desk-rejected 29 papers for "irresponsible" reviewers). 3. Rebuttal Can Save You… Sometimes Many cases where good rebuttals led to score increases and acceptance. But also numerous stories where reviewers didn’t update, or even lowered scores post-rebuttal without clear reason. 4. Meta-Reviewers & ACs Wield Real Power Several stories where ACs overruled reviewers (for both acceptance and rejection). Meta-reviewer “mistakes” (e.g., recommend accept but click reject) — some authors appealed and got the result changed. 5. System Flaws and Community Frustrations Complaints about the “review lottery”, irresponsible/underqualified reviewers, ACs ignoring rebuttal, and unfixable errors. Many hope for peer review reform: more double-blind accountability, reviewer rating, and even rewards for good reviewing (see this arXiv paper proposing reform). Community Quotes & Highlights "Now I believe in luck, not just science." — Anonymous "Desk reject just before notification, it's a heartbreaker." — 877129391241, Zhihu "I got 555, we did it boys." — lifex, Reddit "Three ACs gave Accept, but it was still rejected — I have no words." — 寄寄子, Zhihu "Training loss increases inference time — is this GPT reviewing?" — Knight, Zhihu "Meta-review: Accept. Final Decision: Reject. Reached out, they fixed it." — fall22_cs_throwaway, Reddit Final Thoughts: Is ICCV Peer Review Broken? ICCV 2025 gave us a microcosm of everything good and bad about large-scale peer review: scientific excellence, reviewer burnout, human bias, reviewer heroism, and plenty of randomness. Takeaways: Prepare your best work, but steel yourself for randomness. Test early on https://review.cspaper.org before and after submission to help build reasonable expectation Craft a strong, detailed rebuttal — sometimes it works miracles. If you sense real injustice, appeal or contact your AC, but don’t count on it. Above all: Don’t take a single decision as a final judgment of your science, your skill, or your future. Join the Conversation! What was YOUR ICCV 2025 review experience? Did you spot AI-generated reviews? Did a miracle rebuttal save your work? Is the peer review crisis fixable, or are we doomed to reviewer roulette forever? “Always hoping for the best! But worse case scenario, one can go for a Workshop with a Proceedings Track!” — Reddit [image: peerreview-nickkim.jpg] Let’s keep pushing for better science — and a better system. If you find this article helpful, insightful, or just painfully relatable, upvote and share with your fellow researchers. The struggle is real, and you are not alone!

As a reviewer for the NeurIPS 2025 Datasets & Benchmarks (D&B) track, I’ve been working through my assigned submissions — and, as many of you may relate to, my inbox has been buzzing with notifications from the program chairs. While reviewing remains a thoughtful, human-driven task, this year’s workflow includes a few important upgrades that are worth sharing, especially for researchers who care deeply about the transparency, reproducibility, and ethics of peer review. Here’s a quick behind-the-scenes look at how the process works in 2025 and how it differs from previous years. Automatic Dataset Reports: A New Gatekeeping Assistant One of the most noticeable improvements this year is the automatic generation of a Dataset Reviewer Report for each submission that includes a dataset. This report is not a replacement for human judgment, but rather a helpful tool to assist reviewers in evaluating dataset accessibility, structure, and metadata completeness. This report is based on a metadata format called Croissant, and it checks: Whether the dataset URLs actually work If the dataset files can be downloaded and accessed If a valid license and documentation are included Whether basic ethical and Responsible AI (RAI) information is present ️ Think of this as a checklist that helps filter out incomplete or misleading submissions early on — without you needing to spend your first 30 minutes chasing broken links. You also get Python code snippets auto-generated in the report to help you load and explore the dataset directly from platforms like Kaggle, Hugging Face, or Dataverse. It’s a small touch, but really reduces friction during the review. ️ Responsible Reviewing Is Now Mandatory (Not Just Encouraged) The Responsible Reviewing Initiative is not new, but it’s more strictly enforced this year. Reviewers are now expected to look for the following in each dataset paper: Is the dataset publicly available and reproducible? Are ethical considerations and data limitations addressed? Are RAI fields (like bias, demographic info, or collection methods) present or at least acknowledged? Is the licensing and permission status clear? These were optional or lightly emphasized in previous years, but they now carry real weight in your evaluation — especially for a track that centers on datasets and benchmarks. If a dataset claims to be open but is inaccessible, lacks a license, or ignores potential bias or harm, reviewers are encouraged to flag this as a major concern. ️ Review Process Reminders Here are a few reminders for reviewers in 2025: Don't use LLMs to process or summarize submissions — per NeurIPS’s LLM usage policy, reviewing is strictly human-only. Be proactive in checking for conflicts of interest. Not all COIs are perfectly detected by the system. Every submission matters — even if the topic is outside your direct interests, you’re expected to review it unless there’s a serious reason you cannot (in which case, contact your Area Chair). Watch your assignment list — more papers may get added during the review period. What’s Better Compared to Last Year? Feature 2024 2025 Dataset Accessibility Check Manual by reviewer Auto-checked by metadata report Responsible AI Metadata Encouraged ️ Now explicitly reviewed Review Support Tools Basic 🧰 Code snippets and report summaries Licensing and Ethics Optional in many cases More formally required LLM Policy Vague enforcement Strict ban on use in reviews 🧠 Takeaways for Researchers and Reviewers The D&B track is evolving to match the increasing complexity of data-driven research. If you’re a researcher, this means submitting your dataset now requires more than just a ZIP file on Google Drive — it needs structure, documentation, and ethical awareness. If you’re a reviewer, you now have better tools to assess those aspects — but also more responsibility to do so thoughtfully. All of this helps build a stronger, more reproducible research ecosystem, and makes dataset contributions as robust as model papers.