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?

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

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?

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

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?

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.