Cycle abstraction

Stop choosing a column and start choosing a plan — pick one of five intentions for the whole five-drop cycle, and let a model predict which intention this board should adopt.

The intuition

A search that thinks one column at a time has to rediscover its own strategy every move, and it cannot represent an idea like "spend this cycle digging, then cash in". A human plays in phrases, not syllables.

This lab compresses the decision. Between two rises there are exactly five drops, so it defines five macro options — intentions that are held for a whole cycle:

• clear — take the discs that are available now;
• build — arrange numbers so that a future trigger fires a chain;
• tunnel — dig at the covered discs and open them;
• safety — keep the board low and survive the coming rise;
• balanced — a mixture.

Each option is a different weighting of the same measurements, so "choosing an option" means choosing which of five ready-made evaluators drives the next five moves. The board itself is summarised by 41 features — occupancy, cover counts and altitude, height and roughness, how far triggers are from firing, stored high numbers, low-number clogs, the visible next disc and every column height.

The question the lab asks is narrow and testable: from those 41 numbers alone, can you tell which of the five intentions this board should adopt?

How it works, step by step

1. Collect public positions at cycle boundaries from played games.
2. Label each position five times. For each option, play the cycle out under that intention and then continue under balanced to the horizon, several times, on paired synthetic disc tapes keyed to the visible position. Record how often the branch survived and how long it lasted.
3. Fit two models per option from the 41 features: a logistic model for "does this survive" and a linear model for "how many moves".
4. Test on held-out games with two frozen gates: the survival model's area under the curve must reach 0.75, and the rank correlation between predicted and actual option ordering must reach 0.6.
5. If it passed, a policy could pick its intention at each cycle boundary.

What happened, in plain English

Half of it worked, and it was the half that does not matter.

Predicting whether a board survives turned out to be reasonably easy — 41 public features are enough to tell a healthy board from a doomed one. Ranking which of the five intentions is best for this board was weak. Since the whole point was to choose an intention, the lab was retired.

This is the repository's central failure pattern arriving in a new costume. Being accurate about a position's value is not the same as being accurate about the ordering of the choices available at it — the difference the sibling ranking page is about. Here the "siblings" are not columns but plans, and the result is the same.

The technical record

The experiment index records this as rejected, task-record only: "survival prediction was strong but option ranking was weak." Task-record only means the run was reported in a research conversation and was never promoted into the experiment history. The achieved area under the curve, the achieved rank correlation, and the number of games behind them are not retained anywhere in this repository, so this page quotes none of them. The two gate values are verifiable: cycle-abstraction.cpp requires auc >= 0.75 && kendall >= 0.6 and writes both into its report.

Repository-verified from the source: 41 features indexed by name, five macro options (clear, build, tunnel, safety, balanced), option scoring built from shared potential, clog, danger and tunnel-progress terms plus a scaled score signal, labels produced by holding an option for five drops and continuing under balanced to the horizon on paired synthetic tapes, and per-option logistic and linear models fitted with ridge regularization on the 0x3d70… training range. Tapes are keyed on a hash of the observable state — board, next disc, rise clock, level — so no hidden value and no real future disc is read.

What this taught us, and what is still open

• Compressing the action space does not compress the difficulty. Five options instead of seven columns is a much easier ranking problem on paper, and it was still too hard from these features.
• The idea keeps reappearing because it is probably right. Planning across a complete rise cycle is one of the few things that has repeatedly shown signal in this repository — the constructive complete-cycle planner beat a one-ply policy substantially, and a 12-move constructive horizon beat a seven-move one — while never being stable enough to displace the reference search. The strategy catalog's standing recommendation is to test long-cycle features as bounded corrections to the reference search rather than letting them control a whole game.
• What was ruled out is predicting the best cycle-level intention from these 41 static features. Nothing here tests a search over macro options, or options chosen by anything other than a fitted model.