The first draft 'G' chapter of the geometric deep learning book is live! 🚀

Alice enters the magical, branchy world of Graphs and GNNs 🕸️ (LLMs are there too!)

I've spent 7+ years studying, researching & talking about graphs -- This text is my best attempt at conveying everything i've learnt 💎

4yrs ago, the Geometric Deep Learning proto-book hit the arXiv 📚

Today, we proudly release *Chapter 4* of the GDL Book! 📖 Wrapping up foundations, exceeding the proto-book page count 🚀

Next: deep-dives into the 5 Gs! #⃣🔁🕸️🌐🪢

@mmbronstein.bsky.social @joanbruna.bsky.social @taco-cohen.bsky.social

The recording of my 'LLMs as GNNs' talk is now public (link in thread) 💬🕸️

Thank you to everyone in the GLOW community for having me -- I had a fantastic time! 🌟

(And I look forward to refining this content even further -- especially as new works come trickling in 👀)

This is happening today! 🌟

Join me at the virtual GLOW seminar (5pm CET) for the first public showing of my 'LLMs as GNNs' talk. 💬🕸️

I'm excited to share that we'll have Ilan Price giving a talk at the University of Cambridge on GenCast -- a state-of-the-art model for probabilistic weather forecasting 🌦️⛈️⛅️

If you're in Cambridge next Wednesday (19 Feb), consider joining us -- it's open to all!

All details (+ Zoom link) below!

And so we set out to understand _feedforward_ graphs (i.e. graphs w/o back edges) ⏩

Turns out these graphs are rather understudied for how often they are used in practice! 😮

We work towards a framework to analyse them, which I hope both my GNN and my LLM friends will enjoy 🚀

Exciting news: the NeurIPS Sci4DL Workshop has recognised our softmax paper as a best paper runner-up for its 'Debunking Challenge'! 🚀🧑‍🔬

I'm really grateful to Christos and Federico for tirelessly presenting our work throughout the day, and to all attendees who kindly stopped by our talks/posters! 🙌

A very nice blog from Przemek Pietrzkiewicz, offering thoughts on our recent result in AI for competitive programming 🏆

Przemek co-led the Hash Code contest, which we used as the main test-bed to evaluate our approach 🚀

Worth a read if you want to understand implications of our work! Link below ⬇️

Just a poster I'm very proud of. Mainly because it's the first time I led a theory-focused paper 🥳

Coming soon to NeurIPS Workshops near you (two spotlights!!) 🔦

I unfortunately won't be there myself, but Christos and Federico will be around 🚀

To gather additional evidence that results are robust, we ran our approach on a variant of one of the very few heuristic contests that took place _after_ Gemini 1.5 Flash 002 was released: the AtCoder Heuristic Contest 039.

To our delight, the approach still hillclimbs well! 🏔️

While Hash Code contests are in the past, we've strong reason to believe this is not solution retrieval:

* Gemini isn't given the task statement;
* Solutions must conform to the backbone API (not in the training data);
* Chain of 10+ LLM calls is needed!

OQR'15 (Optimize a Data Center) example:

The system works quite well! 🔥

On _all_ eight previous Hash Code Online Qualification Rounds, our approach breaks into the first percentile of all human competitors' scores (including some of the world's best). 🎉

It even achieves a rank-1 result on five of the past rounds! 🤯

This complementarity is embraced by our approach, which is _synergistic_ 🤝 human competitors write the backbone of a greedy solution, and we ask evolutionary programming and LLMs to evolve the function that steers it!

This division of work streamlines the requirements of the AI system.

There's been a rightful surge of AI-powered competitive programming systems, typically deployed on classical contests such as Codeforces.

While very impressive results have been achieved (ELO ~1,900), they are still significantly away from the highest percentiles of competitors.

A clear step towards achieving my dream: building AI that assists competitive programmers 🧑‍💻

“This is an exciting approach to combine work of human competitive programmers and LLMs, to achieve results that neither would achieve on their own.” --Petr Mitrichev

More details below! 🧵