u/d_edge_sword

The results should be out soon.

Let’s share them, guys.

From my batch (Applied Track)

Total 13 submissions:

- 2 full papers

- 1 short paper accepted

Cheers!

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.

Hi everyone.

First-time reviewer for data mining venues here.

For the applied tracks in ICDM and KDD, the CFP states submissions should be single-blind, showing the author's name and affiliations.

I received some submissions in double-blind (no author names and affiliations). Should they be rejected? How do you handle this?

I am a 6th-year PhD candidate (heading into my 7th) at an R1 university. My research is interdisciplinary, spanning Computer Science (AI), Stats, and Finance.

My timeline was severely disrupted early on: I lost my first 3 years to COVID, and later took a gap year due to family issues, finally resuming full-time in 2025.

At the start of this year, faculty informed me that my funding has run out and my hard program deadline is July next year.

While my last 1.5 years have been highly productive, I am running out of time. I currently have 3 papers under review (all submitted in 2026):

• 1x First-author A* Finance journal (standard turnaround is 2-3 years)
• 2x First-author CS conferences

I also have 2x co-authored (non-first) publications: 1x A* in Finance and 1x A in CS, totaling 100+ citations.

The Problem:
My department strictly requires 3 first-author publications (with at least one A*) to graduate. Because of the brutal 2–3 year review cycle in Finance, my advisor agreed to a compromise: I can graduate if I get 1 reputable A-grade CS acceptance, plus 3 more papers currently under review. He made it clear that he cannot pass me with zero first-authored acceptances. The CS department has no precedent for it, and the committee wouldn't allow it.

Today, I received a rejection from the A-grade CS conference, one with the best venue fit for my research, despite having high scores. (I fked it up) This completely derails my timeline.

Normally, I wouldn't panic over one rejection, I’d just refine and resubmit. But I must get an acceptance this year to apply for graduation next semester before my program expires.

I wouldn't mind dropping out if I wasn't "95% done". My co-author (a Finance AP) even told me, "You've done double the workload of my PhD. In the Finance or stats department, you'd have graduated twice by now." And I had to explain to him that I had tried this conversation with my supervisor multiple times. Comp Sci works differently from Finance and Stats, its impossible to graduate with 0 first-authored publications.

Has anyone been in a similar situation? How did you deal with it? Both coping with the mental stress and the concrete steps you take to resolve it?

Hi, has anyone had issues with Open Review when you try to add long tex code? The markdown just stops compiling?

For example, I want to write a formula like this:

https://preview.redd.it/nfw6slbu74yg1.png?width=527&format=png&auto=webp&s=8a8329f0df6ca92b317c98eade3725ce83e29184

It gets displayed like this:

https://preview.redd.it/nrjcugk584yg1.png?width=731&format=png&auto=webp&s=da92460e650f7bea530d745127aa72d0ecf5acd2

But when I broke the formula into small pieces, like 1 item per line, it worked.

The LaTeX code is as follows:

$$
\mathbb E\left[\left(\widehat q^{DSI}_{\alpha,S,N}-q^\star\right)^2\right]
=
\frac{1}{f^\star(q^\star)^2}
\left(b_q^2+\frac{\sigma_q^2}{K_{\mathrm{eff},q}}\right)
+L^{sam}_{q,N}+o(\cdot),
$$


and


$$
\mathbb E\left[\left(\widehat{ES}^{DSI}_{\alpha,S,N}-ES_\alpha(P^\star)\right)^2\right]
=
\frac{1}{\alpha^2}
\left(b_{ES}^2+\frac{\sigma_{ES}^2}{K_{\mathrm{eff},ES}}\right)
+L^{sam}_{ES,N}+o(\cdot).
$$
```

```

As the title suggests, I received a weak rejection with high confidence from a reviewer who is clearly LLM written, while all 4 other reviewers had given a positive score with low confidence.

Most of the points he raised are trivial and do not apply to my paper. All the baselines he mentioned are irrelevant to my task. They are the exact same points raised when I ran LLM simulations.

He is not replying to my rebuttal. I would like to know how people usually deal with this kind of situation. Do you collect evidence and report him to the AC? If so, how do you collect evidence? When you report him to the AC, do you report him on a low-quality review or LLM usage? Because my understanding is that while using LLM, other than grammar polishing, is not allowed, but it's hard to prove it.

Would be nice if people could share their experiences.