Interaction Scaling: Grounding the Third Axis of Test-Time Compute
There are two standard ways to spend more compute at test time: let a model reason longer, or sample more attempts and keep one. Both share a hidden limit: they are internal. Every extra token comes from the same frozen weights and the same prompt, so neither can tell the model anything it does not already know. We study a third way, interaction: the model proposes an artifact, an external instrument observes how it actually behaves, and the model revises. Each cycle imports a real observation, so interaction breaks through the ceiling the other two hit. We argue that a single variable governs this third axis, grounding, and that it must hold on both sides of the loop. The feedback that d...
| Authors | Bojie Li, Noah Shi |
|---|---|
| Primary category | cs.AI |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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