Thread by @GoodfireAI

> replicate J-space on GLM 5.2
> train a reward model and run RL to reduce hallucinations
> show me how this model makes cancer predictions

Using our platform Silico is like having a team of AI researchers ready to run experiments like these.

Private beta is open now. 🧵 (1/6)

Silico replicated J-space on GLM-5.2 overnight.

It then extended context to ~256k tokens, replicating the key results on multi-hop question answering. (2/6)

Our team spent months developing RLFR, our method which uses probes on a model's internals as reward signals for RL.

Silico reproduced it in 2 days, reducing hallucinations in Qwen3-8B by 37% without capability loss. (3/6)

Silico lets us look inside models to see what they’ve learned.

Using BSFs on protein language models, it found - without supervision - subspaces in the model whose activations correlate with known protein structures. (4/6)

Here, Silico replicated PICASSO on Midnight-12k in one-shot.

PICASSO interprets digital pathology models. It breaks what the model sees into readable concepts, shows which drive its cancer predictions, and simulates how changes to the tissue would alter those predictions. (5/6)

These are just a few examples of what you can do with Silico.

Request access to the private beta here: goodfire.ai/contact