Xaira announced X-Cell it first step toward a virtual cell.

A foundation model that predicts how gene expression changes under causal perturbations — across cell types, conditions, and even unseen biology. Preprint.

This is not trained on observational atlases.

Xaira built X-Atlas/Pisces:

-25.6M perturbed single cells
-Genome-wide CRISPRi
-16 diverse biological contexts
-150K perturbation–context pairs

Key point: this is interventional data, not observational atlases.

That’s what enables causal learning.

Xaira model perturbations as a state transition: control cell → perturbed cell

X-Cell is a diffusion language model that:

1. iteratively refines gene expression
2. models multi-step regulatory cascades
3. improves predictions at inference time

Biology is a process — diffusion naturally fits that. Scaling alone isn’t enough.

Xaira explicitly inject biological knowledge via cross-attention:

-- protein language models
-- gene embeddings from text
-- interaction networks (STRING)
-- dependency maps (DepMap)
-- morphology profiles

This lets the model move beyond pattern matching → mechanistic reasoning.

Across multiple benchmarks, X-Cell shows significant improvement over prior SOTA

Better:
--differential expression prediction
-- fold-change accuracy
-- perturbation specificity

And importantly: it works on held-out perturbations, not just seen ones.

Xaira scaled to 4.9B parameters (X-Cell-Ultra). key findings:

• Performance continues to improve with scale
• Perturbation prediction follows power-law scaling
• Similar behavior to frontier LLMs

This suggests biology is amenable to scaling laws. X-Cell is an early step toward a virtual cell that can guide experiments before they are run.