Virtual ignition

Before choosing a column, imagine dropping a few imaginary discs onto the board to see which arrangements would go off like a firework — and prefer the boards that would.

The intuition

Hand evaluators normally judge chain potential with a static rule of thumb: count the discs that are one addition away from clearing, add something for how those clears might link up, and stop. That rule is a guess about a cascade, made without simulating one.

Virtual ignition replaces the guess with the real thing. It takes the board, places up to a handful of virtual discs on it — discs that do not exist and are marked as such — and asks the actual rules engine what would happen. If a board answers "a five-wave chain that opens four gray discs", it is a board with stored energy. If it answers "nothing", it is not, whatever the static count says.

The measured answer is turned into nine features: how ready the board is to ignite, how much the seed disc itself would clear, how many covered discs would crack and reveal in the first wave, how many discs, reveals and waves would follow downstream, how deep the cascade would run, and how much the total covered population would fall.

How it works, step by step

1. Read the public position — visible board, visible next disc, rise clock.
2. For each candidate ignition point, add a virtual disc and run the real cascade resolver over several stratified guesses about what the hidden gray numbers would turn out to be. Nothing about the actual hidden values is read; the guesses are drawn from a fixed public sampler.
3. Collect the nine energy features and score them with fixed weights, which pay most for downstream reveals (+260), downstream waves (+240) and downstream clears (+160).
4. Add that as a residual to the fair leaf evaluator, scaled by a constant.
5. Use the combined evaluator as the leaf of a depth-3 sparse expectimax search and play the winning column.

What happened, in plain English

It was retired for cost. Simulating hypothetical cascades at every leaf of a three-move search is expensive, the gain it bought was small, and small gains do not justify large costs when the reference search is faster and stronger.

The lab is built as a scale sweep — it plays the same games with the ignition residual switched off and at four increasing strengths — so the retirement is a statement about the whole curve, not one setting.

The technical record

The experiment index records this lab as rejected, task-record only: "the small gain did not justify its substantial search cost." Task-record only means the comparison lives in a research conversation and was never promoted into the experiment history. No score, move count, work figure, or cohort size for that comparison is retained anywhere in this repository, so this page quotes none. What "substantial search cost" means numerically is exactly the thing that is missing.

Repository-verified from the source: src/core/typescript/virtual-ignition.ts extracts nine features with weights +40 (ignition readiness), +40 (seed clear potential), +90 (initial cover cracks), +180 (initial cover reveals), +160 (downstream clears), +260 (downstream cover reveals), +240 (downstream waves), +80 (cascade depth energy) and +160 (cover reduction); it places at most six virtual additions and defaults to seven reveal scenarios. The lab main.ts is pinned to depth 3 with five chance samples, three ignition scenarios, a 1,000,000 work bound, and sweeps residual scales 0, 0.05, 0.1, 0.25, 0.5 over games from the 0x1d70… training range. It reports mean and median score, moves, censored games, work per move and evaluator cache hit rate — the shape of the missing result.

The virtual discs and the reveal guesses are generated from a fixed public sampler keyed on the visible state. No hidden value or future disc is read.

What this taught us, and what is still open

• Measuring chain potential is not the same as being able to afford it. This is the family's clearest cost-side negative: a more faithful feature can be strictly better per evaluation and still lose, because the search around it gets smaller.
• A negative for one price, not for the idea. The rejection is of this configuration — full cascade simulation at every leaf of a depth-3 search. An incremental or cached formulation, or the same signal used only at the root, was not tested.
• The record is thinner than it should be. A rejection whose retained form is one sentence cannot be re-examined, re-powered, or refuted. Anyone resuming this line should expect to re-run the scale sweep rather than build on it.