I'm presenting the poster today. Details below:

Fri, Dec 5, 2025
11:00 AM – 2:00 PM PST
Exhibit Hall C,D,E #4505

Pic: (fancy) knots at USS midway museum near SD convention center

✨ Why it matters

KnotGym gives us a lightweight yet expressive testbed for multi-modal long-horizon reasoning and planning.

📄 Paper: arxiv.org/abs/2505.18028
🔗 Website: lil-lab.github.io/knotgym

Joint work with @yoavartzi.com

🧠 What can agents do in KnotGym?

➡️ Untangle a knot
➡️ Tie a goal knot
➡️ Convert one knot into another

All within Gym + MuJoCo, easy to run, hard to solve.

Even strong RL baselines and VLMs cannot beat random at cross number # X=3 (though they fail for different reasons).

🔗 Why knots?

Knots are simple to see but deep to reason about.

✔ Verifiable outcomes
✔ Structured complexity (crossing number # X)
✔ A ladder of difficulty for generalization

Perfect for studying long-horizon visual reasoning and test-time scaling in visual space.

also imo this is a habit that is cultivated by constant practice (say, from local collaboration/mentorship or OSS). Instead of a whopping 12-week course, a workshop talk or informal tricks-sharing is perhaps more suitable

The Internet has almost too many resources on general SE best practices (super useful for code release). What's lacking are good programming practices in the context of day-to-day research, e.g., versioning datasets, tracking experiments, reporting prelim findings, reacting to constant pivots

Why bother coming up with an "artificial" project when there are natural ones and the goal (I assume) is to train better researchers anyway?

I actually relate to much of the presentation on state management.

Jupyter shines in plotting and interactive demoing. E.g., a use case not fulfilled by console or scripts: prompt engineering. Jupyter (1) does not reload model weights and (2) can fold/clear historical long outputs like logits

A PhD *student* paranoid with code. I guess that’s what makes me a student 🥲

You were blessed with a codebase that's easy to work with, or the ability to build one. IMO factoring is tricky for different, ever-shifting research goals. See a discussion on "single-file implementation" and "Does modularity help RL libraries?" at iclr-blog-track.github.io/2022/03/25/p...

What’s wrong with Jupyter notebooks 😂

That’s quite a lot of investment in a course for phds lol. How about allowing collaborated projects in your graduate seminar?

Also collaborating with others in the same repo motivated both of us to write better code than we would otherwise.

Speaking as a phd paranoid with code:

goodresearch.dev is good.

A guilty pleasure of mine is reading not only good research repo, but also their full git history if released. Factored code is not always easy to change and a big refactor commit says something.

Some misread it as geopolitics instead of racism.

And caring for others, that’s not exactly part of a researcher’s job description or perf review.

I made up the second one to save myself from greater disappointment.

All I am saying is I don't assume a prior definition, nor do I observe your latent thought process

I’m not sure what conclusion I can draw from this poll.

And disclaimer - this is absolutely not affiliated with neurips.

Credit goes to everyone who participated in this mini poll. Thank you - you made my day!