Phase horizon

Judge a board by how it will look when the next row rises, not by how it looks now — a board one drop from a rise is in far more trouble than the same board five drops away.

The intuition

Most hand evaluators score the board in front of them. But in this game the board is on a clock: every five drops, everything shifts up and seven new covered discs appear underneath. Two boards can look identical and be in completely different situations because one of them has four drops of breathing room and the other has one.

Phase horizon makes the rise clock a first-class input. Its central feature is not occupancy but projected occupancy debt: assume the policy manages the sustainable 2.4 clears per drop between now and the rise, project the board forward to that moment, and score that. A crowded board with time to work is fine; a comfortable board about to be squeezed is not.

The same trick is applied to covered discs. Residual cover debt asks how many covers will still be closed when the next seven arrive, and a separate "imminent" altitude term sharpens the penalty for high covers as the rise gets close.

How it works, step by step

1. Read the public position — visible board, visible next disc, and how many drops remain before the rise.
2. Project forward to the rise at the sustainable clear rate and extract twelve phase features: projected occupancy debt, residual cover debt, two cover-altitude debts, peak height risk, low-cap load, adjacent low-cap load, direct build inventory, quiet build options, quiet direct gain, trigger readiness and release readiness.
3. Add them to the ordinary hand evaluator's features with fixed weights — the largest are −900 for peak height risk and +600 for the best quiet building move available.
4. Use the result as the leaf of a bounded sparse expectimax search.
5. Play the winning column.

What happened, in plain English

The frozen policy helped in some games and was retired because it missed the bar it had set for itself. Its own gate was a training-set mean of 400,000 points; it did not get there, and the calibration and validation seed ranges were never opened.

The lab deserves credit for how it failed. The weight sweep is deliberately tiny — each family of weights is halved or doubled around one fixed centre, nothing else — the selected weights are locked before calibration is allowed to run, and the validation and final seed ranges are rejected by the program itself rather than by convention. That is what a small honest tuning run looks like, and it is why "it did not reach 400,000" is a usable result instead of a mystery.

The technical record

The experiment index records this as rejected, task-record only: "the frozen policy improved some games but failed its 400,000-point gate." Task-record only means the run is reported in a research conversation and was never promoted into the experiment history. The achieved mean, the number of games, and the per-game outcomes are not retained anywhere in this repository. The gate value is not a guess, though: REQUIRED_TRAINING_MEAN = 400_000 is a constant in the lab's own main.ts, alongside MATERIAL_IMPROVEMENT = 1.05.

Repository-verified from the source. src/core/typescript/phase-horizon-evaluator.ts defines REQUIRED_CLEAR_RATE = 1 + BOARD_SIZE / MOVES_PER_LEVEL and REQUIRED_REVEAL_RATE = BOARD_SIZE / MOVES_PER_LEVEL — the same 2.4 and 1.4 conservation arithmetic used by gray throughput — an operating band of two discs per column and three rows of height, and twelve weighted features from −900 (peak height risk) to +600 (quiet direct gain). The lab sweeps them on the 0x1d70… training range with 2 pilot and 16 confirmation games at a 500-move cap, a 1,000,000 work bound and a −1,000,000 terminal utility; the 0x5d70… calibration range is used only after the weights are frozen, and the 0x7d70… validation and 0xd700… final ranges are hard rejected by the program.

The evaluator is also the baseline comparator inside the risk-sensitive lab, and the ledger's "hand-crafted blend" experiment calibrated a learned value function's scale against it (experiment history).

The evaluator reads only the visible board, next disc and rise clock.

What this taught us, and what is still open

• Phase is a real and cheap signal. Conditioning on the rise clock is now standard across the repository: the reference search's leaf penalises danger near a rise, and later families use phase-conditioned weight blocks. This lab is where that became routine.
• A projection is only as good as the rate you assume. Projected occupancy debt assumes the policy will sustain 2.4 clears per drop. The reference policy sustains about 1.973 (finding 01, exploratory, development tier), so the projection is systematically optimistic about every board it scores. Nothing in the retained record tests whether that matters.
• What was ruled out is one small sweep around one centre, on training seeds, judged by a self-imposed mean. That is a narrow rejection, and a wider one would need paired games against the unchanged reference search.