Bruno Neri

Technical Leader - Artificial Intelligence and Machine Learning Enthusiast - Senior Software Engineer @altenitalia

[Bruno Neri @neribr]

If you wanna change the world, start off by making your bed. If you make your bed every morning, you have accomplished the first task of the day and it will encourage you to do another task ...

#leadership

https://t.co/rCFKOCwFGE

[Daniel Holmberg @dholmbrg]

The ocean is inherently chaotic, yet existing data-driven ocean models produce deterministic forecasts. In our new preprint, we introduce Njord, a probabilistic graph neural network for ensemble ocean forecasting.

Link: https://t.co/CMd0JfSd4C

A couple highlights below 🧵

[Nina Miolane 🦋 @ninamiolane.bsky.social @ninamiolane]

Can geometry help explain how neuronal signals create consciousness?🤔🌐

Loved diving into this massive question with the brilliant @claire_i_webb thanks to @longnow & @berggruenInst !

Watch our full conversation here ⬇️
@ucsbece @geometric_intel @ai_ucsb

[Marco Pavone @drmapavone]

Introducing FRAX: Fast Robot Kinematics and Dynamics in #JAX — to be presented at the 2026 IEEE International Conference on Robotics and Automation (ICRA) Frontiers of Optimization for Robotics (FOR) Workshop.

FRAX delivers extremely fast (low-microsecond) execution for common inverse-kinematic and inverse-dynamic control workloads, with a pure Python codebase that can achieve up to 5× faster performance than MuJoCo or Pinocchio Python bindings in several settings.

At the same time, FRAX is fully differentiable and seamlessly compatible with CPU, GPU, and TPU execution through #JAX — enabling scalable workflows spanning robotics, control, planning, and machine learning.

Our broader goal is to help bridge the gap between modern AI tooling and robotics computation, making it easier to develop scalable #Physical #AI systems.

This also makes FRAX a great complement to CBFPY (https://t.co/o1UrsnE01b), our package for robot safety and control barrier functions.

Kudos to @danielpmorton for leading this effort.

If you’ll be at ICRA, reach out! The FOR Workshop is on Monday, June 1, and we’ll have a poster there.

💻 GitHub: https://t.co/epQUbFLGdo
📄 Paper: https://t.co/qWvrvBuRVO

#Robotics #PhysicalAI #JAX #DifferentiablePhysics #MachineLearning #AutonomousSystems #GPU #Simulation #ICRA

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[Mathilde Papillon🦋 mathildepapillon .bsky .social @mathildepapillo]

🏆 The 2026 Topological Deep Learning Challenge is officially live, now in its 4th edition! 🏆
This year’s theme is “Bridging the Gap” between the GNN and TDL worlds.

Win incredible prizes including up to $1000 in cash 💸 and AI research internships!

Submission deadline: Aug. 1

[Mathematica @mathemetica]

Decoding the Geometry of Stability.

In linear dynamical systems, the long-term fate of every trajectory is encoded in the eigenvalues (λ) of the system matrix.

This classification in the complex plane shows exactly how eigenvalue locations dictate the phase portrait:

- Real eigenvalues -> Nodes (sinks/sources) or saddles
- Complex conjugate eigenvalues -> Rotation: spiral foci or centers

Key rules:
- Re(λ) < 0: Trajectories collapse to the equilibrium -> asymptotically stable
- Re(λ) > 0: Trajectories explode away -> unstable
- Re(λ) = 0 (pure imaginary): Closed orbits -> marginally stable (centers in 2D)

[Simone Scardapane @s_scardapane]

*Anatomy of a ML Ecosystem: 2 Million Models on Hugging Face*
by @BenDLaufer @didaoh

I missed this cool paper from last year! They use evolutionary biology methods to study how models on HF evolve over time based on their metadata and model cards.

https://t.co/WWouAkxdFw

[Simone Scardapane @s_scardapane]

*Alice goes to Japan!*

The book is available for purchase from https://t.co/GHWbarIJAk! As always, price is almost all printing (except a ☕). 🙃

https://t.co/MgqiJ86HDA微分可能な不思議の国のアリスの冒険-ニューラルネットワーク設計入門書-第1巻-Simone-Scardapane/dp/B0D9QHS5NG/

[Bruno Neri @neribr]

Keep going down the rabbit-hole with Alice guided by @PetarV_93 @mmbronstein @TacoCohen

#geometricdeeplearing

[Petar Veličković @PetarV_93]

oh, did i say chapter? i meant _chapters_

We've just released draft Chapter 6 (Grids) and Chapter 7 (Group Convolutions on Homogeneous Spaces) of the GDL Book

Alice's journeys in geometric wonderland continue #️⃣🌍

[Petar Veličković @PetarV_93]

This paper kicked off our team's studies into the intricate relationship LLMs have with confidence. Now landed in @NatMachIntell 🚀

Give it a read, esp if you enjoy CogSci-style analyses of LLMs 🧠

Thoroughly impressed by Dharsh's leadership on this work! More outputs soon 👀

[Bruno Neri @neribr]

@PetarV_93 Nice news! Some deadline in mind?

[Bruno Neri @neribr]

"A Mechanistic Analysis of Looped Reasoning Language Models" by @HughBlayney , @arroyo_alvr , Johan Samir Obando Ceron, @pcastr, @AaronCourville, @mmbronstein , @epomqo

Paper: https://t.co/B7LaHXvFDG

#machinelearning

[Simone Scardapane @s_scardapane]

*Learning without training: The implicit dynamics of in-context learning*
by Dherin et al.

An interesting work that tries to explains in-context learning by "contextual" low-rank updates on the MLP components.

https://t.co/xQDk2p8VDu

[Thomas Kipf @tkipf]

There's finally a community implementation of Neural Assets (in PyTorch)! Go check it out 👇

Neural Assets was one of the first (and maybe even the first scalable?) solution(s) to the long-standing problem of multi-entity consistency in visual generative models. One of the most fun projects I had the chance to work on (with the amazing @Dazitu_616).

[Bruno Neri @neribr]

If you want to improve your skills about flow matching and diffusion model take a look at this amazing course by @peholderrieth

[Peter Holderrieth @peholderrieth]

🚀MIT Flow Matching and Diffusion Lecture 2026 Released (https://t.co/bKgs2wghvY)!

We just released our new MIT 2026 course on flow matching and diffusion models! We teach the full stack of modern AI image, video, protein generators - theory and practice. We include:
📺 Videos: Step-by-step derivations.
📝 Notes: Mathematically self-contained lecture notes
💻 Coding: Hands-on exercises for every component

We fully improved last years’ iteration and added new topics: latent spaces, diffusion transformers, building language models with discrete diffusion models.

Everything is available here: https://t.co/bKgs2wghvY

A huge thanks to Tommi Jaakkola for his support in making this class possible and Ashay Athalye (MIT SOUL) for the incredible production! Was fun to do this with @RShprints!

#MachineLearning #GenerativeAI #MIT #DiffusionModels #AI

[MIT CSAIL @MIT_CSAIL]

A guide to remembering everything you read: https://t.co/b4DwxJnUki

v/@CuriousMindsHub

[Peyman Milanfar @docmilanfar]

@neribr @2ptmvd Link to the paper

https://t.co/dd0M6oGj6w

[Bruno Neri @neribr]

@docmilanfar @2ptmvd sorry 😅

[Bruno Neri @neribr]

"The Geometry of Noise: Why Diffusion Models Don't Need Noise Conditioning" by Mojtaba Sahraee-Ardakan, @2ptmvd , @docmilanfar

#machinelearning

[Bruno Neri @neribr]

"Categorical Flow Maps" by @daan_roos_ , @osclsd , @FEijkelboom , @mmbronstein, @wellingmax, @ismaililkanc , @LucaAmb , @jwvdm

Paper: https://t.co/BQyGxLnwc8

#machinelearning