SKILL.state: State Instead of History

I found SKILL.state: Scalable Long-Horizon Agent Skills (Badhe, Tiwari & Chung) interesting. It proposes a different approach than most SOTA agent implementations: keep the current state, not the full execution history.

At each step, the model gets the skill, the current state, and the latest observation. It produces a state update and an action. The runtime applies the update, executes the action, and starts the next step from the new state.

The key idea is that state becomes the source of truth. The model does not reconstruct the present from a long transcript. It discards the reasoning behind each step right after that step commits.

In a normal history-based agent, each call carries most of the previous calls, so the context window eventually overflows. Per-step prompt size grows with every step, and total tokens over a run grow with the square of the step count. SKILL.state holds each step to three fixed inputs instead: the skill spec, the current state (a plain JSON object), and the latest observation. A state update applies as a JSON Merge Patch, so null deletes a field, an object merges recursively, and anything else replaces it. Per-step prompt size stays constant, so tokens over a run grow linearly instead of quadratically.

I wrote a quick implementation based on the paper, to test whether it holds up. Its test suite has one that asserts per-step prompt size stays constant across many steps rather than growing — a direct check of the paper’s core claim.