I began to like the word "Workspace".

It is the internal area where a model holds, evaluates, and revises ideas before producing an answer.

Based on Ilya Sutskever's recent emphasis on generalization, value functions, and continual learning, it seems that SSI may be building a model that can repeatedly improve this workspace, judge whether its thinking is moving in the right direction, and retain useful discoveries.

Anthropic's Jacobian Lens offers a related clue: it helps researchers understand how changes in middle-layer representations shape a model's eventual output.

The Jacobian Lens reads the workspace. Latent reasoning updates the workspace. A value function tells it which direction to move. Continual learning preserves what worked.

Highly recommended a blog from @ZilongZheng 's BIGAI Research Team.

This excellent blog explains the latent-reasoning part of that picture:

How a model can use extra test-time compute to improve its hidden state before answering.

Blog:

Reasoning as Value-Guided Latent-Space Optimization

https://latentreasoning.github.io/test-time-latent-reasoning/