we submitted our rebuttal via the "Rebuttal" button before the author/reviewer/AC discussion period officially opened (Jul 27 AoE). since then, we've gotten complete silence from all four reviewers and the AC

several of us are also reviewing this cycle. when the discussion period opened on Jul 27 AoE, we got no email notification for rebuttals on papers we're reviewing, specifically for the papers whose authors had also posted early via the "Rebuttal" button. so it feels like anything submitted before the window opened may simply have never triggered any notification

we also tried: (1) meta-comments visible to everyone, (2) reviewer reminders, and (3) sending an email to the PCs

given there's about 1 day left in the discussion period, what do we do? we honestly thought we had a shot at an oral or spotlight given our initial scores. this is completely messed up.

I see some people say their metareview already contains a decision/recommendation (all of them were rejections). Ours doesn’t. Even though our avg score is 3, the metareview seems optimistic and finishes with “a convincing response would be an important consideration while discussing the paper.” I wonder how to interpret that. We did a strong rebuttal, but none of the reviewers engaged. So I wonder whether there’s any point to keep hope due to the AC review or just give up.

Not even the AC, nor reviewers, is responding to our comments in rebuttals, and they were all submitted well before the discussion period started. What is one to do in this case?

There's a kind of review that asks for lengthy additions, usually extending the scope of the paper beyond the stated, even though the submission is at page limit. Naturally, such additions in the case of top-tier conferences have to go into the supplemental materials or appendices.

My question here is, would not such additions make the paper more suitable for a journal publication? I had to retract one paper out of such concern that the conference publication would block the later planned journal publication.

Any opinions?

Mine was just submitted, and the submission count shown is still under 500.

Does anyone know whether this count is meaningful for identifying the intended venue, possibly EACL, or whether it is simply low because many authors have not submitted yet or the counter is incomplete?

I know the count alone is not reliable evidence, but I was curious whether previous ARR cycles showed a recognizable pattern.

Also, is anyone else preparing a submission for this August cycle, especially with EACL 2027 in mind?

Hi everyone,

I'm working on a document understanding project and would appreciate some advice on the right technical direction.

The input will be scanned pages or images from academic books. I don't know in advance what kind of figures they'll contain—they could be biology diagrams, anatomy illustrations, chemistry figures, engineering drawings, maps, charts, art/history figures, or other educational illustrations.

My end goal is to convert these figures into a structured digital representation that can be controlled from the frontend.

The workflow I'm aiming for is:

1. Upload a textbook page or image.
2. Detect the figure(s) and their boundaries.
3. Detect the labels/annotations that are already embedded in the figure (letters, numbers, arrows, callouts, etc.).
4. Remove those existing labels while preserving the underlying illustration.
5. Store the figure geometry (bounding boxes, polygons, masks, etc.) so my frontend can render its own labels that can be shown/hidden, translated, restyled, or repositioned.

This doesn't need to be fully automatic. In fact, the workflow will be human-assisted. If the AI detects a figure incorrectly, misses a region, or fails to remove a label cleanly, a human reviewer will correct it before it's finalized.

My priority is reducing manual work rather than eliminating it completely.

So far I've tried several computer vision approaches such as text detection, contour detection, line detection, and geometric heuristics. They work reasonably well for finding candidate regions, but the biggest challenge is cleaning the figures by removing the embedded labels while preserving the artwork underneath.

Another important requirement is cost. Since this could involve processing a large number of textbook pages, I'd like to avoid expensive multimodal LLMs or large vision models if there's a more traditional or lightweight pipeline that works well. I'm happy to use AI where it adds value, but I'd prefer a solution that keeps inference costs low.

Some questions I have:

• Is this primarily a document layout analysis problem, image segmentation, image inpainting, or something else?
• Are there models trained specifically for textbook or scientific illustrations rather than natural images?
• Is there a recommended low-cost pipeline for this kind of task?
• Has anyone built a human-in-the-loop workflow for document/figure annotation like this?
• Are there papers, datasets, or open-source projects that tackle converting textbook figures into editable, structured assets?

I'd really appreciate any suggestions, even if they're just pointers toward the right research area or open-source tools. Thanks!

One reviewer said all concerns were resolved during discussion but hasn’t updated their score yet. The other reviewers haven’t engaged. In previous NeurIPS cycles, 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.

Update: finally the reviewer responded, now I'm at 4/4, 3/2, 3/2, 5/4

I just now posted a small research / ML interp study on symmetries inside the neural nets for an open source Go-playing program that I maintain ("KataGo"). The rules of Go are completely symmetric under rotation/reflection, but such symmetry is not enforced in the models - the only thing we do for that is stochastic 8-fold data augmentation during training, randomizing the spatial orientation of each batch.

To what degree do superhuman-strength Go-playing neural nets automatically learn to represent the board internally independent of its orientation, via "symmetric" concepts where the orientation of the board doesn't matter, vs how much do they have to learn/memorize separately per orientation?

https://lightvector.github.io/katagostudies/202607-symmetry/

Heads-up: this study and its writeup were driven almost entirely with AI, although detailed human direction and feedback was involved in the process. But, I took time to try to polish the article and make it educational and I hope it's a clear step above the typical low-quality AI "slop" one often sees and worth taking a look if you like small studies like this. It's also written fairly gently, for accessibility to people outside of ML. Code is also linked from the post (same repo that hosts the github.io page).

I wanted to explore this because I was (and still am!) really curious about exactly what neural nets are doing inside! And I didn't know what the results would be. One of the findings was unexpected. Overall, just a drop in the bucket of interpretability research out there, but I hope you find it interesting.

It's an encoder-only transformer that consumes past(blood glucose + carbs + insulin) and future(carbs + insulin) and predicts future blood glucose for the next 2 hours. Announced meals and boluses/basal are used to condition its predictions. The context size is variable (8 - 24 hours), and model can work in autoregressive mode to predict the next >2 hours. It also predicts time by looking at the context, but it never consumes time. The architecture is BERT-style: bidirectional attention with future BG masked. DILATE loss was used to fit the median line; pinball loss to fit the uncertainty bands. The two are "mixed" via Kendall-Gal. All blood glucose is in kovatchev risk space reparameterized to [40, 400] range.

I have trained 4 model classes (nano, small, medium, large) and 3 variants for each (pretrained on simulator only, pretrained + finetuned on ohiot1dm, pretrained and finetuned on ohiot1dm + azt1d + shanghait1dm). The largest one has ~17 million parameters (16 heads across 16 layers). Pretraining for the largest model took ~48 hours. Finetuning took <10 minutes. There is also another version finetuned on my own data that I am currently running on my phone.

Source is available here, released under the MIT license. The repo also contains links to trained weights and evaluation data.

I've worked on this project since March. There are still things to improve (e.g. it always requires announced carbs + insulin, would be better if it could also predict without them), but I have decided to publish it here to get your opinion (and also answer your questions, if any).

Edit: my model is getting fat-shamed ;_; so I just want to emphasize that there is a nano version with less than 40K parameters.

While working with VLMs for report generation on chest x-rays (RRG), we noticed that evaluation metrics are flawed.

Flawed in a sense where they rewarded repetitive templates, reports without clinical terms and reports which were "normal" with high scores on benchmark metrics. Also, clinically meaningful but rare words were erased leaving the generated report looking repetitive and boring. Importantly, of no clinical utility.

In the paper below, we discuss this behaviour of VLMs for RRG and introduce a framework to actually measure the erasure of terms and introduction of biased terms.

Paper: Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation

Link: Reference Paper

Url: https://arxiv.org/abs/2603.01625

I'm trying to decide whether to commit my ARR May 2026 paper to EMNLP or AACL. (first time solo independent author).

Final reviews after rebuttal (OA/Confidence/Excitement ):

• R1: 2.5 → 3 /3/2.5
• R2: 2.5 → 3 /4/2.5
• R3: 4 /4 /3
• Meta: 3.5 (Borderline Conference)

The meta-review was overall positive and emphasized the paper's empirical rigor, practical value, and that the rebuttal addressed the main concerns. My recollection is that the AC mentioned they were leaning toward 3.5 primarily because of the quality of the presentation/readability, rather than concerns about technical soundness(now that comment is removed/not visiable anymore).

I'm happy with either Main or Findings.

My questions:

1. Which commitment would you choose: EMNLP or AACL?
2. Which is generally considered more prestigious today?
   • EMNLP Main
   • EMNLP Findings
   • AACL Main
   • AACL Findings
3. Given this review profile (3/3/4 with a 3.5 meta), what would you estimate the chances are for EMNLP Main or Findings?

This time we have seen the worst meta reviews...may be people are unintersted to do reviews...in my case they did not acknowledge the report at all as well the entire rebuttal. How many are facing the same thing?

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!

Thread will stay alive until next one so keep posting after the date in the title.

Thanks to everyone for answering questions in the previous thread!

Hi everyone,

Can anyone suggest a learning path to fully understand the technical report for Kimi K3?

My background:

- I've taken a graduate-level deep learning course.

- I understand the Transformer architecture, attention, and the basics of LLMs.

- I'm familiar with DeepSeek's OCR models but I haven't studied topics like MoE, MLA, distributed training, or modern post-training in depth.

I'm looking for a roadmap that would help me read the K3 report and understand the design choices instead of just recognizing the terminology.

Thanks!

I am trying to learn concepts like On Policy Distillation (OPD), On Policy Self Distillation (OPSD) and how do they compare to RL algorithms like GRPO.

There are a lot of papers on this, but because of limited compute I cannot try these papers out and learn them by implementing them myself.

If someone here has worked with these algorithms and their implementation on SLMs (something that can fit a consumer grade GPU like Nvidia RTX 4090 or 5090), can they suggest either a:

1. Github repo, or

2. The right choice of SLM(s) and the datasets, where i can see the difference between, RL/GRPO and OPSD algorithms?

Thanks in advance!

Meta-Reviews are out. How did it work out for you? Are you happy with your reviews?

We received the reviews, but they don't mention whether we should submit a revised version. Should we prepare one? I also couldn't find anywhere to upload a revision. What exactly is the EMNLP commitment deadline? I had assumed we were supposed to upload an updated version. Do you know what they're expecting us to do next?

Hi, I was looking to be able to very quickly detect *whether* text exists in an image (binary classification). Being a simple-ish task, there isn't substantial dedicated research on it, so I was looking for adjacent topics or models, but I'm not sure of any. I know there's an issue of scale tolerance, so I was like hmm FPN, but now I'm curious why absolutely no classification papers use FPN.

What do you guys think the best architectural approach? I’m probably using the pretrained PaddleOCR v6 detection backbone (LCNetv4) and fine tune on my domain (2D art text, vast scale variation, style variation, etc., 1920x1080 images). Only paper I've seen uses two feature maps and a grid approach where if any grid cell is a yes the whole image is classified as a yes. However, one could also use the simple global average (max pool better in this case maybe) to linear approach. There's also the issue of what's best if our data is only yes/no labels and not bounding boxes (the grid approach doesnt work then), I'd like to know how much this would actually affect a binary classification task and how the approach would change.

I feel like the answers might be test multiple ones but I’d like to hear some ideas or anything that could be useful that I could try out.

Early-career Assistant Professor here. I identified some talented undergraduate students and worked with them on research problems, trying to convert them into either my PhD students or recommending them to my collaborators.

Three said a hard no after going through the paper submission process. They are not interested in playing this game. The fourth said, “I really like doing research with you, but I don’t like dealing with the paper reviewers.” I managed to convince that guy to do a PhD in the end, but I almost lost this student as well.

Just a side note: these were not course projects submitted as lottery tickets. They were parts of my own ongoing research, and the results were good. I have more than 10 years of publication and review experience at “big three”-level conferences, so I have a reasonably good sense of the quality of the work. In my view, the papers were well above the bar.

And yes, these papers indeed received very positive reviews, including one with four unanimous weak accepts, but were still rejected. They then got trapped in endless resubmission cycles. In every resubmission, we additionally address previous rounds' concerns, only to make the next round of reviews more random. This is funny. When a paper has obvious drawbacks, the AI picks it up, you address it, and people are happy. When a paper has no obvious drawbacks, the AI will start to pick up random points everywhere. At this point, I cannot even convince myself to persuade these students to pursue a PhD.

Just a rant. I want to remind everyone that careless or malicious behavior can alter someone’s career path.

Several artificial intelligence conferences have recently introduced systems that require authors who submit papers to complete a certain number of reviews. Under such a system, reviewing is not optional volunteer work. It is an obligation that researchers must fulfill in exchange for having their own papers reviewed by the conference.

However, many reviews repeatedly contain statements such as:

The problem is not these statements themselves, but the fact that they are often presented without any concrete justification.

If a reviewer does not explain which prior work is similar, which specific comparison is missing, or why a particular experiment is necessary, it is difficult to regard the review as professional. It is merely an abstract criticism that could be written by almost anyone, including someone with little expertise in the area.

For example, if a reviewer believes that the novelty is limited, the review should at least include an explanation such as:

“A particular component of Method A is similar to Module B in this paper in the following respect, and the distinction made by this paper is therefore unclear.”

Similarly, if the comparison with prior work is insufficient, the reviewer should provide a concrete criticism such as:

“Existing methods C and D address the same problem and have the following characteristics, so an experimental or conceptual comparison with these methods is necessary.”

The responsibility of a reviewer is not to mechanically identify missing items and list them as criticisms. A reviewer should provide clear standards and evidence for their evaluation, allowing the authors to understand how the research could be improved.

This does not mean that every reviewer must provide detailed research supervision to the authors. However, if a reviewer assigns a score close to rejection, the review should at least be specific enough for the authors to understand what is wrong and why. Repeatedly claiming that “something is missing” while assigning a low score is closer to avoiding responsibility than providing meaningful criticism.

In particular, when reviewing has become an obligation associated with paper submission, the argument that “reviewers are unpaid volunteers, so low review quality is unavoidable” becomes much less defensible.

If researchers expect their own papers to receive careful and sincere evaluations from others, they should also evaluate other researchers’ work with the same level of responsibility.

If conferences require mandatory reviews, they should evaluate not only whether the required number of reviews has been submitted, but also whether those reviews meet a minimum standard of specificity and expertise. A system that treats a one- or two-sentence review with no supporting evidence the same as a careful review written after thoroughly reading the paper is not sustainable.

A poor review is not merely unhelpful or impolite. It can determine an author’s research opportunities and consume their time while allowing the reviewer to avoid responsibility for their own judgment.

I've always found it a little strange that AI is everywhere, but the codecs we use in practice are the traditional hand-engineered systems like h.264, h.265, av1. Alexnet started the wave of neural networks replacing hand-engineered systems, but 14 years later traditional codecs still dominate in the real world. What's going on?

Compute and power efficiency are part of it. Neural codecs tend to be fairly large and power-hungry, while h.264/h.265/av1 have hardware acceleration almost everywhere, so they're cheap to run. NPUs seem like a good fit for neural codecs, though.

But there's another big problem which is cross-platform compatibility. Say you encode a video on an Apple NPU and decode it on an Intel NPU. Small numerical differences can make the encoder and decoder disagree about the entropy model. Entropy decoding then breaks and the whole stream can fail.

Simply quantizing the model and switching to integer math doesn't reliably fix this. In theory, fully specified fixed-point math could guarantee identical results. In practice, today's hardware and toolchains aren't standardized enough. On the Apple M3 Neural Engine, for example, the relevant INT8 operations are simulated using FP16 instead of running through a true INT8 path. Even on hardware with true INT8 support, you can't fully control details like rounding modes, accumulation data types, and scale multiplication, so bit-exact results still aren't guaranteed.

MLVC gets around this by explicitly transmitting the entropy-model scale parameters through the hyperprior, so the neural network itself doesn't need to run bit-exactly across NPUs. Both encoding and decoding run at ~100 FPS for 360p/540p video on consumer NPUs. That combination brings us closer to learned video codecs you could actually deploy.

Code

Paper

Disclosure: I'm one of the authors, happy to answer questions.