Los Alamos chess game 2 (after P-K3) is solved; black wins in 21 moves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a defining event for the field of Artificial Intelligence (AI), the first game of chess skill between a human and computer took place in 1956 (Chess Review (1957) 13–17; The Machine Plays Chess? (1978) Pergamon Press). In this match, Dr Martin Kruskal from Princeton University played White against the MANIAC I computer at Los Alamos Scientific Laboratory in New Mexico, programmed by Paul Stein and Mark Wells. Due to the very limited capacity of computers at the time, which couldn’t handle a full 8 × 8 chess board, the competitors played “Los Alamos Chess”, a minichess variant using a 6 × 6 board without bishops. For this game, White played without a queen, opened with P-K3 and ultimately won against the machine opponent in 38 moves. Here we show that Black can force a win in 21 moves.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it