---
title: Trajectory throughput
family: baselines-diagnostics
summary: Runs a player that can see hidden numbers beside public-information players on the same games, then compares their boards every five moves to show what self-sustaining play looks like.
status: completed
evidence: task-record only
reads: oracle
---

Line up an ordinary player and a player that is allowed to cheat on the same
games, chop both games into five-move windows, and compare the windows: what is
the privileged player doing that the public-information player is not?

<EvidenceLabel status="completed" evidence="task-record only" reads="oracle" />

<Callout title="This comparison includes a privileged player" tone="warn">
One of the arms is a perfect-information planner: it reads the hidden numbers
under the gray discs. That is not a legal Drop7 policy and can never be one —
it is a **ceiling and a diagnostic**, used here to describe what a much better
trajectory looks like. No score produced by that arm is a policy result. See
[oracle in the glossary](/learn/glossary).
</Callout>

## The intuition

The natural question ("why do our policies die?") is hard to answer by
staring at final scores, because the answer arrives dozens of moves after the
mistake. The trick used here is to compare *windows* rather than games.

A window is one complete five-drop cycle, ending exactly at a rise. Because
both players get the same seed and are compared window against window at the
same point in the game, differences in what happened inside a window are
differences in play, not differences in luck or in how long each survived.

For each window the program records what the board looked like and what flowed
through it: how many gray discs were cracked, how many were revealed, how many
numbered discs cleared, how many chain waves and how deep the deepest was, how
many boards were cleared outright, how many moves were "quiet" (building
without scoring), how many produced more than one wave, and how much stored
trigger potential was spent. Alongside those it records board-shape measures —
height, occupancy, cover load, direct and latent chain potential, and clog
patterns such as adjacent 1s and triple 2s — and, for the searching arms, the
search depth and work per move.

The comparison then asks which of those quantities most separates the
privileged arm from the public-information arms. Whatever that is, is a candidate
description of "a game that sustains itself".

## How it works

1. Play the same seeds with each arm: the perfect-information planner (a
   depth-4 beam of width 128 over known hidden values), the hand-written
   `combined` evaluator inside a bounded search, and optionally a trained DQN
   checkpoint.
2. Cut every game at completed five-move boundaries and emit one record per
   window per arm.
3. Match windows across arms by seed and by window position, so every
   comparison is like-for-like.
4. Rank the measured quantities by how strongly they separate the arms. The
   report's own note is careful about this: the separation figure is the mean
   difference divided by the standard deviation of per-seed mean differences,
   "a ranking aid, not a causal estimate", precisely so that repeated windows
   from one game do not masquerade as independent games.
5. Use training and calibration seeds only; validation and final seeds are
   never opened.

## What happened

This is the analysis that produced the flow targets the rest of the repository
quotes: roughly **2.4 numbered clears and 1.4 reveals per move** as the region
associated with games that keep going. The
[research status](/docs/research/status) carries them with an explicit warning
attached — "treat these as diagnostic targets from limited runs, not proven
universal thresholds", and this page repeats the warning, because the run that
produced them survives only as a task record.

Two later, independently recorded results speak to the same numbers and are
worth reading next to them:

- [Finding 01](/docs/exploratory/finding-01-score-is-survival) *derives* 2.400
  and 1.400 rather than measuring them: every five-move cycle puts five placed
  discs plus a seven-disc covered row onto a 49-cell board, so 12/5 and 7/5 are
  the break-even rates. It measures the depth-4 reference at 1.973 clears and
  1.090 reveals per move over 64 paired games: a structural deficit of 18% and
  22%.
- [Finding 06](/docs/exploratory/finding-06-flow-ceiling) measures a
  clairvoyant planner at 2.3663 clears and 1.3832 reveals per move on 64 fresh
  tapes. It also carries two published correction notices, including one
  stating that **no legal policy tested reached flow balance**; the best legal
  arm reached 2.2309 clears per move, 93.0% of the requirement.

So the target region this diagnostic pointed at has held up as a description of
what the ceiling looks like. What no retained record shows is a legal policy
reaching it.

<TechnicalDetails title="The technical record">

**Status: completed; task-record only.** The
[experiment index](/docs/research/experiment-index) records: "this analysis
motivated the approximate 2.4-clear and 1.4-reveal sustainability targets."
There is no ledger entry, protocol, artifact hash or retained per-window data;
[audit 04](/docs/exploratory/audit-04-blind-spots) §B.2 lists it as
task-record only and notes that it is **the only file in the repository that
counts board clears**.

Defaults, from the source: training seeds from `0x2d700000`, calibration seeds
from `0x5d700000`, validation seeds from `0x7d700000` reserved and untouched,
4 games each, a 100-move cap, window = 5 moves, oracle depth 4 with beam width
128, baseline search depth 4 with a 20,000-unit work cap. The privileged arm is
`planOracleMove` from
[`approaches/oracle-curriculum/perfect-information-oracle`](/approaches/oracle-curriculum).
The report begins with its own warning string about the oracle's status.

Both of the corroborating findings above are exploratory documents at the
`development`/`pilot` tier, not ledger results, and finding 06 explicitly warns
that its eight original master tapes favour long games. The clairvoyant figures
there are stated to be unaffected by that bias; the fair depth-4 comparator on
those tapes is not.

Source: `main.ts`.

</TechnicalDetails>

## What this taught us, and what is still open

- Comparing matched five-move windows, rather than whole games, is a way to see
  a policy's mistakes near where they happen.
- A privileged planner shows what a sustained game looks like. It does not show
  how to get there, and none of the legal policies measured since have.
- The 2.4 / 1.4 pair has survived contact with two later analyses, one of which
  derives it from the board's arithmetic, but the run on this page is a task
  record and cannot be re-derived from anything retained here.