Granite 4.0 Nano: Just how small can you go?

Granite 4.1 LLMs: How They’re Built

Granite 4.2 LLMs: How They're Built

Introducing GPT-5.4 mini and nano

GPT-5. 4 mini and nano are smaller, faster versions of GPT-5.

Finer is Better (with the Right Scaling)

arXiv:2605. 08565v2 Announce Type: replace Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats.

Reproducible Reservoir Computing with Thermally Driven Superparamagnets: Controlling Temperature Sensitivity

arXiv:2607. 12840v1 Announce Type: cross Abstract: Unconventional computing systems must demonstrate robust performance under real-world environmental conditions to enable practical deployments.

Introducing Nano Banana Pro

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.

Gemma 3n fully available in the open-source ecosystem!

Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason!

MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design

MOCLIP (Metasurface Optics Contrastive Learning Pretrained) is a nanophotonic foundation model that unifies metasurface geometry and spectra in a shared latent space using contrastive learning on an ImageNet‑1K–sized experimental dataset. It enables high‑throughput zero‑shot inverse design, predicting 0.2 million samples per second and allowing the design of a full 4‑inch wafer of high‑density metasurfaces in minutes. The model also supports generative latent‑space optimization with 97 % accuracy and demonstrates an optical information storage concept achieving 0.1 Gbit/mm², six times higher than commercial optical media.

Introducing Gemma 3 270M: The compact model for hyper-efficient AI

Today, we're adding a new, highly specialized tool to the Gemma 3 toolkit: Gemma 3 270M, a compact, 270-million parameter model.

Unraveling the Size Determination Mechanism of Nanocrystal Synthesis via Interpretable Neural Networks

arXiv:2608. 14734v1 Announce Type: cross Abstract: Deep learning models of nanocrystal synthesis enable the prediction of size and shape by encoding precursors and reaction conditions.