escalate

name	escalate
description	Handle the two situations the greedy ratchet cannot: route safety-critical or high-risk changes to human review, and switch search strategy when the loop plateaus (greedy local search -> grid / tree / evolutionary). Use from inside a ratchet-experiment loop.

Status: v0 scaffold — unverified. Behavior not yet validated end-to-end in this repo. See ../../STATUS.md.

escalate

The keep/revert ratchet is greedy and autonomous. Two conditions require escalation.

Trigger 1 — safety-critical / high-risk change

If a proposed change touches a safety-relevant path (planner, controller, anything that could cause real-world harm) or is otherwise high-risk:

• Stop autonomous merging. Treat the result as candidate evidence only.
• Open a human-review request with: the diff, the measured effect, the evaluator used, and the residual risks.
• Proceed only after a human approves. See ../../AGENTS.md.

Trigger 2 — the loop has plateaued (local-search trap)

If N consecutive experiments fail to improve the metric beyond measured noise, the greedy ratchet is likely stuck in a local optimum (see ../../docs/pitfalls.md). Switch strategy:

• Factorial grid — sweep several variants in parallel to catch interaction effects (see ../../src/talos/orchestration.py).
• Tree search — branch from the best node, not just the latest (AIDE-style).
• Evolutionary search — a population with novelty rejection (ShinkaEvolve / OpenEvolve-style) when single-lineage search is exhausted.
• Or hand a moonshot to a human to seed a bigger architectural bet.

Output

Either a human-review request (Trigger 1) or a switch to a broader search procedure (Trigger 2), recorded in the ledger so the change of strategy is auditable.