We developed a library to make logical reasoning embarrasingly parallel on the GPU.

For those at ICLR 🇸🇬: you can get the juicy details tomorrow (poster #414 at 15:00). Hope to see you there!

What is reasoning and have LLMs learned it?

We answer these two questions in our new pre-print using the phase transition in random 3-SAT.
arxiv.org/abs/2504.039...

Together with @rishihazra95.bsky.social, @gabventurato.bsky.social and @lucderaedt.bsky.social

Here is also a longer thread:

If you're at #AAAI2025, come check out our demo on neurosymbolic reinforcement learning with probabilistic logic shields 🤖 Tomorrow (Sat, March 1) from 12:30–2:30 PM during the poster session 💻

We all know backpropagation can calculate gradients, but it can do much more than that!

Come to my #AAAI2025 oral tomorrow (11:45, Room 119B) to learn more.

If you're at #AAAI2025, come check out my talk tomorrow (Fri, Feb 28) from 9:30–10:45 AM in the Machine Learning track! 👨🏻‍🏫

📍 Swing by our poster later in the day for more discussion!

🚀 Applications include:
- Safe reinforcement learning
- Logical control in language models
- Temporal reasoning with strong guarantees

Let's discuss! Thoughts, questions, or collaborations? ⬇️ #neurosymbolic #AAAI2025

🙌 @lennertds.bsky.social, @giuseppemarra.bsky.social, @lucderaedt.bsky.social

🔬 In experiments, NeSy-MMs outperform existing neurosymbolic & deep models on both generative & discriminative tasks.

They generalize better to out-of-distribution settings and allow test-time constraint adaptation, making them robust & versatile.

NeSy-MMs integrate:
✅ Relational probabilistic reasoning
✅ Logical constraints
✅ Neural learning
✅ Approximate Bayesian inference

We introduce a novel differentiable particle filter that enables efficient inference & learning while maintaining logical consistency.

Markov models are great for sequential problems, but deep models struggle with constraints, and neurosymbolic AI doesn’t scale.

NeSy-MMs bridge this gap: a new class of differentiable models that provably satisfy relational logical constraints while scaling efficiently.

🔥 Can AI reason over time while following logical rules in relational domains? We will present Relational Neurosymbolic Markov Models (NeSy-MMs) next week at #AAAI2025! 🎉

📜 Paper: arxiv.org/pdf/2412.13023
💻 Code: github.com/ML-KULeuven/...

🧵⬇️

🚀 Do you care about safe AI? Do you want RL agents that are both smart & trustworthy?

At #AAAI2025, we present our demo for neurosymbolic RL—combining deep learning with probabilistic logic shields for safer, interpretable AI in complex environments. 🏰🔥
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🦕The 19th conference on Neurosymbolic AI will be in beautiful Santa Cruz (CA, USA), September 8-10, 2025!

CFP is now out: 2025.nesyconf.org/call-for-pap...
🚨 Paper deadline: Feb 28 (abstract), March 7 (full)

#neurosymbolic #NeSy2025

I made a starter pack with the people doing something related to Neurosymbolic AI that I could find.

Let me know if I missed you!
go.bsky.app/RMJ8q3i

Meet our researchers from the DTAI lab at KU Leuven!

Using this starter pack, you can keep up with all the AI research from our PhD students, post-docs, professors and alumni 🦋