Read docs/PROMPT_cheap_diversity.md

Prompt — cheap diversity knobs (tournament size + mutation + random immigrants), curve as the test

The fitness curve collapses early (best = median by ~gen 10, then flat) — premature convergence. Add the cheap diversity levers before considering the island model. Verified against the code. Defaults must preserve current behaviour exactly. pytest + tsc after.

Verified current state (engine_v2/gp.py)

run_gp_v2(..., population_size=150, tournament_k=3, elitism=5, p_mutate=0.7, ...). _tournament_select (line 31) picks k random contenders and returns the best. Main loop (121-204): evaluate → rank → carry elitism elites → fill the rest via _tournament_select ×2 + crossover + mutate. There is no random-immigrant mechanism today.

Part A — add the three levers to run_gp_v2

1. Tournament size — tournament_k is already a param. Lowering it (3 → 2) reduces selection pressure → slower takeover → more diversity. Keep the default 3.
2. Mutation — p_mutate is already a param (0.7). Allow raising it (e.g. 0.85). Keep the default 0.7.
3. Random immigrants (new) — add immigrant_fraction: float = 0.0. When > 0, each generation reserve round(immigrant_fraction * population_size) slots in the new population for fresh random programs drawn from ramped_population (same rng/seed, same grammar/objective constraints as init) instead of crossover+mutation offspring. Fill them after the elites, before/among the offspring; never displace the elites. Default 0.0 ⇒ behaviour unchanged.

Part B — one UI toggle to drive them (so you can A/B the curve)

• RunRequest gains diversity: bool = False. The _worker maps diversity=True → tournament_k=2, p_mutate=0.85, immigrant_fraction=0.10 (tune if needed); diversity=False → current defaults unchanged.
• Frontend: a Parameters checkbox "Maintain diversity" (default OFF). Tooltip in plain English: "Off: the population can collapse to near-clones early (the fitness curve's best and median lines meet and go flat). On: lowers selection pressure and injects fresh random programs each generation, so the population keeps exploring — watch the best-vs-median gap stay open longer."

Part C — the test is the curve

The success criterion is visible in the Live view fitness curve: with Maintain diversity ON, the best and median lines should stay separated for many more generations (the population doesn't collapse to clones by ~gen 10), versus OFF where they meet early. Also confirm the winner's held-out AUROC doesn't meaningfully drop (diversity should preserve or improve detection, not hurt it). Re-run HPV coherence-on with the toggle off vs on and compare the curves.

CONSTRAINTS

• Defaults preserve current behaviour byte-for-byte (immigrant_fraction 0, tournament_k 3, p_mutate 0.7) so existing runs are unchanged. No airgap/API-shape break beyond the additive diversity field. Airgap untouched (this is search-internal; opaque IDs throughout).

Checkpoint

• run_gp_v2 accepts immigrant_fraction; with it >0, fresh random programs enter each generation without displacing elites.
• The "Maintain diversity" toggle (default off) flips tournament_k / p_mutate / immigrant_fraction.
• With the toggle ON, the fitness curve's best-vs-median gap visibly persists longer; winner held-out is not degraded.
• pytest green (defaults unchanged), tsc clean, airgap untouched.