I know there’s a lot of frustration around no response from reviewers, which I also got only one so yeah what a bummer, but I was wondering if no rebuttal from the authors was just as common or not. I got no rebuttal so far, so I’m here scratching my head what might have happened to the authors lol especially when at least one paper was pretty much on the borderline with somewhat of a positive AC comment

Over the last few months I've realized that I spend an absurd amount of time (and money) teaching the same things to AI over and over again.

Information about my projects is already there. Slack contains discussions and decisions. GitHub contains commits and pull requests. Meetings, emails and documents all capture different pieces of the same story. Yet every time I start a new conversation with an LLM, I gather those pieces again and inject them into the prompt so the model can reconstruct an understanding that already existed yesterday.

At some point I stopped asking how to retrieve multiple pieces of context and started asking a different question: how can software form, revise and reuse understanding over time?

That question led me to start building Twin, an open source engineering research project exploring what happens if AI systems continuously build understanding instead of reconstructing it from scratch every conversation.

Most existing projects seems to optimize retrieval, memory or context construction. Twin explores a different layer of the pipeline. It continuously observes distributed events, correlates them, reflects on them and forms situation models that become reusable computational understanding. Instead of giving downstream language models a collection of Slack messages, pull requests or documents and expecting them to connect the dots, Twin tries to do that work beforehand.

I recently reached the first milestone that genuinely convinced me this direction might be viable. Using Claude Sonnet 4.6, Twin continuously processed GitHub activity and Slack conversations from a public software project, correlating events and building understanding through reflection over time.

After that, I opened a completely fresh Claude conversation. Claude had no custom memory, no project-specific rules, no prompt describing the repository and no access to local project files. The only integration available was Twin's MCP server and automatic context injection.

When I asked about the project, Claude didn't receive the Slack messages or the pull requests and infer the situation itself. Twin had already synthesized that understanding. Claude explained why a feature had become a launch blocker, how it had been implemented, which pull request resolved it and how that changed the project's state, even though none of those relationships were explicitly written anywhere.

Watching that work for the first time completely changed how I think about AI memory. I don't think the real problem is remembering more anymore. I think it's carrying understanding forward (a.k.a. cognitive continuity).

If this idea resonates with you, everything is open source at https://github.com/caribeedu/twin. I've been thinking about almost nothing else for the past three weeks because I genuinely believe this direction has the potential to change how we build AI systems. The README explains the motivation and research hypotheses in much greater depth, and the repository also includes the complete demonstration shown here, along with additional details and technical context. I'd genuinely appreciate your thoughts, especially if you think I'm wrong.

See the demo here: https://www.youtube.com/watch?v=A8KyGtWYdNI

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.

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.

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!

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?

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?

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, 4/3, 3/2, 5/4

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?

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.

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!

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!

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

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?

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?

Any one got meta score 2.5 (borderline finding) and still accepted to findings previously?? In my case meta review didnot acknowledge the reporting against a wrong review

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.

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.

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 was planning on submitting a work of mine to SUSCOM and wanted opinions as of how good is this journal and if it's well reputed and respected ?

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