Semi-Supervised Tile Embeddings: A General, Multigame Level Representation
Why this work is in the frame
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Bibliographic record
Abstract
Representing video game levels for level generation and analysis tasks remains an open problem. Existing approaches generally rely on hand-authoring or are game-specific. Tile embeddings are a general machine learned-representation for tile-based game levels, however they have thus far relied solely upon hand-authored representations of levels for training data. In this paper, we introduce Semi-Supervised Tile Embeddings (SSTE) which make use of semi-supervised learning to allow for training on levels lacking human authored representations. We evaluate SSTE over many experiments, finding that it performs equivalently or better than existing tile embeddings. Thus SSTE stands as the first general machine-learned level representation that can scale without requiring additional human labor.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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