Arbor, Autonomous Optimization via Hypothesis Tree Refinement

Overview

This skill runs an Autonomous Optimization (AO) loop: starting from an existing artifact and a measurable objective, improve it through many rounds of experiment and evaluation, without step-by-step human supervision, while checking for overfitting to the development signal. It's the right tool when the bottleneck isn't writing one good change, but organizing dozens of trials so that lessons accumulate instead of evaporating.

It implements Hypothesis Tree Refinement (HTR) from Arbor (Jin et al., 2026). The key idea: keep the research state in a persistent hypothesis tree rather than in conversation history. Each node binds a hypothesis, the distilled insight it produced, and a pointer to the artifact version that realizes it. You play the long-lived coordinator that owns this tree and decides where to search; short-lived executor subagents test one hypothesis each in isolated git worktrees and report back. A held-out merge gate admits a change only when it improves on a test evaluator executors do not optimize against. This is what turns trial-and-error into cumulative, auditable research.

Use the scripts/tree.py state manager for all the bookkeeping (creating nodes, writing evidence, propagating insights, pruning, the merge gate, the Observe projection). It records scores and decisions; it does not run evaluators, create worktrees, verify Git refs, or merge Git branches. Only the coordinator writes shared state, serially; executors return evidence. The helper has no multi-writer locking. Its JSON schema is separate from upstream Arbor checkpoints.

When to use this skill