Xaira says causal drug-discovery models need causal data, not just scale

Latent Space · rss · 2026-07-22

Latent Space discusses Xaira’s X-Cell drug-discovery model and why causal modeling needs causal data.

The team found that scaling a model on a limited dataset eventually hit an information ceiling: test loss flattened while training loss kept improving.
Their response was to build X-Atlas, a much richer dataset generated through large-scale CRISPR-based experiments.
With roughly 30x more information, the model can continue scaling with both parameters and compute.
The episode also covers why the team moved away from autoregression toward diffusion, how the system generalizes to real human-cell experiments, and why it beats previous linear baselines.

The central idea is that better biology models may depend less on parameter scaling and more on collecting causal, information-rich data.

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