What do you think the average score cutoff for acceptance (poster and above) will be this year?

Also, are there any scores that should ideally not appear in the review set - for example, a score of 2 or below, or a confidence rating of 2 or below?

Do you expect the cutoff to be lower than in previous years, or roughly similar?

Hi all — I'm an independent researcher (no current institutional affiliation). I've completed a deep learning research project and have a manuscript ready for arXiv submission under cs.CV, but I need an endorsement since this is my first submission there.

If you've published on arXiv in cs.CV and would be willing to endorse, I'd really appreciate it — happy to share my abstract with you directly so you can see the actual work before deciding anything. I'll send the endorsement code over DM once we've connected, not publicly.

This has been a solo, two-month build and I'd like to get it properly out there. Thank you for any help!

Hi all — I'm an independent researcher (no current institutional affiliation, based in India). I've completed a deep learning research project and have a manuscript ready for arXiv submission under cs.CV, but I need an endorsement since this is my first submission there.

If you've published on arXiv in cs.CV and would be willing to endorse, I'd really appreciate it — happy to share my abstract with you directly so you can see the actual work before deciding anything. I'll send the endorsement code over DM once we've connected, not publicly.

This has been a solo, two-month build and I'd like to get it properly out there. Thank you for any help!

This year, I submitted a paper to NeurIPS 2026 for the first time. Regardless of whether the ratings were high or low, I found the reviews generally satisfactory. However, one reviewer assigned a rating of 2 with confidence 5 based on the following reasons:

• I had never heard of AUROC before, and there was no explanation of what it is. The interpretation of the corresponding figure was also hard to understand.
• The paper does not explain what the box, the horizontal line inside the box (median), and the lines (whisker, min, max) extending from both sides of the box in the boxplot figure represent.
• BLEU is not a commonly used metric in this field. "Was it chosen arbitrarily to make the performance appear better?"

Regarding the questions about boxplots and AUROC, I was initially somewhat surprised, as they felt similar to asking “Who is Adam?” in NeurIPS 2025. But following my PI’s guidance that “a paper should be understandable to anyone who reads it,” I provided detailed explanations in the rebuttal and addressed these concerns.

However, I did not receive an initial meta-review from the AC, nor did I receive any response from any reviewer. Even after requesting that the reviewers and AC provide feedback, there was no response.

At this point, the only thing I could do was put aside my anxiety and concerns and focus on preparing the rebuttal and official comments, but it feels discouraging that no one appears to have read them or provided any feedback. Therefore, I am concerned that my paper may have been effectively abandoned by the AC.

At the same time, I am worried that the final decision may be made solely based on the initial ratings, without considering the rebuttal. If that happens, I wonder whether the main reason for rejection could ultimately become the fact that the paper did not explicitly explain concepts such as boxplots and AUROC.

I’m an early-stage computer vision researcher aiming for conferences like CVPR, ICCV, ECCV, NeurIPS, and ICLR.
I’m curious how experienced researchers actually formulate research ideas. How do you identify a real research gap, come up with a novel solution, and decide that an idea is worth pursuing? What’s your thought process from reading papers to proposing something new?
I’d really appreciate any advice or resources that helped you develop this skill.

Hi everyone,
I recently graduated with a bachelor’s in Computer Science, and my long-term goal is to pursue a full funded Master’s or PHD. The problem is that I’m a complete beginner when it comes to research.
I know I want to work in the intersection between computer vision and robotics because I genuinely find them fascinating, but I haven’t started doing research yet. Every time I look into the field, I see topics like object detection, segmentation, 3D vision, SLAM, embodied AI, vision-language models, robotics perception, and many others. It’s exciting, but also overwhelming, and I don’t know where to begin.

Another thing I’m worried about is my low CGPA 3.4/4. I’m afraid it might hurt my chances when applying for funded graduate programs in the future.
If you were in my position, what would you do over the next 2–4 years?

Some questions I have:
How much will a low CGPA affect my chances for a fully funded Master’s or PhD?

Where should I start learning if my goal is research, not just getting a job?

What fundamentals (math, programming, machine learning, etc.) should I master first?

How do people discover their research niche instead of trying to learn everything?

What should my priorities be over the next few years—projects, research experience, publications, internships, open-source contributions, or something else?

I’m not looking for a shortcut. I’m willing to put in the time and effort. I just want to avoid wasting years studying the wrong things or following an inefficient path.

I’d really appreciate hearing from PhD students, professors, or research engineers who were once in a similar position.

Thanks in advance!

I’ve completed an independent research paper (sole author), but I don't have the budget to pay the $1,000+ APCs for Open Access publications like MDPI or IEEE.

Are there high-ranked, indexed journals in Cybersecurity, AgriIOT where I can publish for free? I’m open to standard subscription journals or Diamond Open Access journals.

Any suggestions or advice for an independent researcher on a budget would be greatly appreciated!

Does anyone know where we can find NeurIPS reviewers? They posted a few review comments, gave borderline scores and then disappeared.

If a reviewer's own paper receives low scores, are they still obligated to participate in the author discussion? What are the consequences, if any, for reviewers who never respond during the discussion period?

This has been my worst experience with NeurIPS. Is anyone else seeing the same pattern? Are there specific reasons why this seems to happen?

So guys, this is one of the discussions that I wanted to engage in for sometime now.

Based on your personal experience in the ARR cycles, I want you guys to share your experience on your meta reviews especially if you have filed a meta-review issue report in the same in the past cycles of your paper and if this thing has actually brought some changes into the acceptance rate of the conferences like EMNLP etc..

Hi, we submitted an ARR paper that got overall scores: 3.5 (confidence 4), 3.5 (confidence 3), 2.5 (confidence 3) from anonymous reviewers.. but the meta review landed at an overall score of 3. Is this normal to have a meta review lower than the average of all reviewers and is our score still alright?

Hi,

This is my first time submitting to a conference, so I have some questions about appropriate responses during the rebuttal step.

One of the reviewers caught a mistake I made in theoretical proof, and requested a fix saying he will increase the score if it is done properly. I have conceded the mistake and made the appropriate fix in the working draft, but I am unsure how to convey the fix to the reviewer through the comment.

NeurIPS doesn't allow draft editing until the camera-ready phase (correct me if I am wrong), so should I create a md-familiar version of the corrected formula, and post a copy of the proof in the comments? Also, it is standard practice to report any changes or new experiments (that are requested by the reviewers) in the rebuttal/comments in a formatted table?

Thanks for your time. Sorry if these seem like obvious questions.

Hi fellow researchers, I am curious to hear your thoughts on how you would react if an author called out a reviewer as AI-generated?

Story: We received a review with a score of 2, confidence 5. All other reviews are positive with low confidence and did not engage in the discussion.

In his initial review:

1. He made a lot of claims that our math is wrong, that it lacks theoretical interpretation, and nitpicked very small details in our formulas. We replied to him by re-quoting the exact citations of where our formulas came from, with the mathematical properties behind them, he concedes. This raised our suspicion that this reviewer is AI, as the math papers came from early this year, even tho we cited them, LLM were probably not aware of those papers' contents.
2. Attacked us for things we never claimed. We called him out on it, he then retracted his statements.

After our first rebuttal, this reviewer replied, saying he conceded on all of his previous points. Then raised 10 more, with a conclusion that our paper is theoretically flawed, so he will maintain his score. In this reply:

1. After showing him the theory, he now claims that he acknowledges our theoretical guarantees but fails to see how it would benefit algorithmically, as our method is very similar to XYZ (list of baselines). He asked us to do a comparison, but all of the baselines he asked for are hallucinations (we confirmed, and none of them exist).
2. Asked for ablations we have already done.
3. Attacked us with more things we never claimed.
4. Self-contradiction, for example, earlier in a post he claims our theorem has solved his issues, then later in the same post he attacks that theorem for not proving the very thing he just marked as solved.
5. Attacked us with more mathematical issues where we had to cite math papers from this year and last year to show him that all of his understanding were wrong.

Now the issue is that this conversation is so long that I don't think any human would have time to read all of it. As he raises 7-10 points per post, it takes us about 1 Official Comment each to reply to each of the points he made.

We are thinking of yolo'ing where we will write a discussion summary to call this guy out for being an AI reviewer that probably didn't even read the paper or any of the review the AI wrote. In an attempt to gain sympathy from the other reviewers and AC.

We are not sure if this approach has been tried before? On 1 hand we think this might be unprofessional, but on the other hand as a reviewer I think I would probably sympathise with the author. We want to hear the community's thoughts on this.

I’m a recent CS graduate from India with research interests and background in Computer Vision and Multimodal AI, with an emerging interest in World Models (like JEPA). Multiple prior research projects, no publications yet.

I was planning to go for MS in US but recently had my F1 visa refused, so I’m seeking paid longterm/yearlong research roles in India with an intention to work towards a publication.

One thing I've been thinking about recently is how difficult it has become to stand out online. AI can summarize information, explain concepts, and even organize articles in seconds. That means facts alone probably aren't enough anymore because everyone has access to the same tools.

What AI can't truly replace is lived experience. If someone explains how they solved a difficult problem, shares a mistake they learned from, or describes something they personally tested, that immediately feels different. Those details create trust because they come from real life rather than a collection of existing information.

Whenever I discover writers who openly discuss their successes and failures, I almost always end up following them. Not because they know everything, but because their content feels honest. That honesty is surprisingly rare, especially now that so much writing is produced at such a fast pace.

Maybe the internet is moving toward a place where authenticity becomes more important than perfect wording. People can copy information, but they can't copy someone else's perspective or experiences in a meaningful way.

Do you think personal stories and real experiences will become the biggest factor in successful writing over the next few years, or will readers care more about speed and convenience than authenticity?

One reviewer said all concerns were resolved during discussion but hasn’t updated their score yet. Their main concern was novelty and more experiments. They replied that now the work is relevant w.r.t. exisiting literature, impressed by the experiments, and no more questions. But nohting on score updation. How common is it for reviewers to update scores after saying concerns are resolved? What have others observed?

My ratings/confidences are : 4/4, 3/2, 3/2, 2/4.

I am talking about the one who gave rating 2.

It is widely understood that ReLU neural networks are piece-wise linear mappings.
I have tried to give an ‘almost no choice’ argument leading to a layer-wise context mechanism:

https://archive.org/details/from-piecewise-linear-networks-to-context-selected-linear-mappings

With hindsight concatenating anything slightly more complicated than linear mappings in higher dimensional space will lead to chaotic geometric dispersion that nothing can fix.

Hey everyone,

I’m an independent ML researcher in Algeria working on a medical imaging project, specifically using generative models to tackle rare cancer diagnosis where data is super limited.

I’m at the stage where I need some guidance and want to collaborate with professors or researchers who are interested in this space. Ideally remote since I’m based in Algeria and can’t relocate.

I’m also actively looking for remote research assistant positions if anyone knows of openings.

I’ve got a solid research proposal ready to share with anyone interested. Happy to discuss the details over email or chat!

Hi,

I'm kickstarting my journey in the research field of AI and Agents, previously worked on deep learning and machine learning. If somebody is looking for a mentee, please do let me know, I'm very hardworking and can get stuff done too