Microsoft Research has unveiled Quine, a research system combining a multimodal 'world model' of biology with an interactive harness linking AI models, scientific literature, lab tools, and researchers. Built jointly across data types such as genomics, proteins, chemistry, RNA/cell state, and bioimaging, the model aims to predict how biological systems respond to interventions before costly lab experiments are run. Working with the Broad Institute of MIT and Harvard, Microsoft used Quine to rank thousands of compounds for their potential to shift pancreatic cancer cells between therapeutic states, and the top-ranked candidates were validated in wet-lab assays within a single weekend. Microsoft is now opening a 'Quine Fellows' program to give selected scientists early access, while cautioning that the system is experimental, research-only, and not intended for clinical use.
A Google Cloud TPU engineering team worked with Ai2 to rebuild Olmo 3 7B's pre-training from scratch using MaxText, Google's JAX/XLA training framework, running on TPUs instead of Ai2's original PyTorch/GPU setup. They matched Ai2's published results not just on the training loss curve but on four independent held-out evaluation surfaces across the full ~5.93-trillion-token, 1.41-million-step stage-1 run plus the stage-2 annealing phase. Along the way they ported Olmo 3's unusual architecture (reordered-norm blocks, QK-norm, 3:1 sliding/global attention ratio) into MaxText and caught a data-loader bug that had been quietly inflating apparent performance through memorization rather than genuine learning.
Google engineers describe how they implemented sparse spatio-temporal attention for video diffusion models on TPU v6e chips, converting the theoretical sparsity of the Sparse VideoGen (SVG) approach into real hardware speedups. Through a progression of kernel optimizations (full/boundary tile specialization, tile-size tuning, and mask-tile alignment), they reduced attention kernel latency from 96.37ms (naive sparse) to 32.76ms, a 2.40x speedup over dense Splash Attention on a single TPU v6e chip, using 75.6K tokens, 10 heads, and head dimension 128.
Google DeepMind unveiled SynthID Bio, a watermarking technology that embeds an imperceptible, verifiable signature into AI-designed proteins and 3D structures without compromising their biological function. The system was tested on protein binders (VEGF-A, SARS-CoV-2 spike RBD, PD-L1) using AlphaProteo and a modified ProteinMPNN, and also fine-tuned into AlphaFold 3's diffusion network for watermarking predicted 3D structures. DeepMind is also collaborating with Stanford's Hie lab and Arc Institute to apply the approach to Evo 2, a genomic model, to watermark designed bacteriophage genomes. The goal is to strengthen biosecurity by helping DNA synthesis screening providers and public databases verify the provenance of AI-generated biological designs.