GraphML News (Feb 23rd) - Achira, AI Co-Scientist, Evo-2,

The announcements of Thinking Machines, Grok-3, and Majorana-1 saturated the media this week, but there has been a good bunch of science-related news, too.

💸 Achira AI, a startup focusing on foundation simulation models for drug discovery, came out of the stealth mode with $30M seed funding from Dimension, Amplify, NVIDIA, and Compound. Founded by John Chodera (Sloan Kettering Institute), Achira aims at “creating a new class of simulation models that blend geometric deep learning, physics, quantum chemistry, and statistical mechanics into advanced potentials and generative models”. Achira will be competing with DE Shaw Research on the market of fast MD simulations, sounds interesting 📈

🧬 Arc Institute announced Evo-2, a family of foundation models trained on DNA of 100k species. Based on StripedHyena 2 (a hybrid attention-convolution architecture), Evo-2 ingests sequences of up to 1M of context length and packs most modern LLM engineering practices (no surpise that the CTO of OpenAI spent his sabbatical at Arc) - training on 9T tokens on 2048 H100s using a custom framework for hybrid models, available in 7B and 40B sizes. Everything is available on Github including two accompanying preprints focusing on the ML side of things and computational genomics side.

🤖 Google Cloud AI, Google Research, and Google DeepMind announced AI Co-Scientist - a multi-agent based system for hypothesis generation, providing research overviews, and generating plans for experiments. Powered by Deep Research and equipped with tool usage, AI Co-Scientist demonstrates benefits of test-time compute scaling and was probed in three applications: drug repurposing, treatment target discovery, and explaining mechanisms for antimicrobial resistance. There will be more published results in specialized journals in the next few weeks.

In a similar vein, Stanford researchers announced Popper agent for automated hypothesis validation and sequential falsification with several practical examples in biology, economics, and sociology. It’s fully open-source so you can plug in any LLM via vLLM / SGLang / llama.cpp

Weekend reading:

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning by Álvaro Arroyo, Alessio Gravina et al feat. Michael Bronstein - an approach to GNN theory from the SSM and recurrence point of view

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction by Xiang Fu and FAIR Chemistry - a new SOTA on MatBench Discovery, MPTraj, and MDR Phonon

Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs by Batu El, Deepro Choudhury feat. our own Chaitanya K. Joshi - among other things they found that GTs learn attention matrices quite different from the input graph you train the model on.