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 the field of General Game Playing (GGP), artificial agents (bots) may be required to play never-before-seen games with less than one minute to initialize and train. Although tabula rasa approaches, like Monte-Carlo Tree Search, are popular in this domain, they do not leverage information from the many different games that a bot has previously encountered. A major barrier to transfer learning has been the difficulty in identifying similar features in the rule descriptions of two different games. We present two methods, called MMap and LMap, for heuristically approximating a distance between two games' graphs, and producing a mapping for the symbols of one to the other, thereby enabling transfer. We evaluate the effectiveness of these methods across a variety of transfer scenarios, and find that both methods are far more accurate than a simpler baseline mapper. MMap is found to be more robust than LMap, but LMap is much faster, and so more suitable for general use in GGP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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