Improving modeling of other agents using tentative stereotypes and compactification of observations
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
We investigate possible improvements to modeling other agents based on observed situation-action pairs and the nearest neighbor rule. Tentative stereotype models allow for good predictions of a modeled agent's behavior even after few observations. Periodic revaluation of the chosen stereotype and the potential for switching between different stereotypes or to the observation based model aids in dealing with very similar (but not identical) stereotypes and agents that do not conform to any stereotype. Finally, compactification of observations keeps the application of the model efficient by reducing comparisons within the nearest neighbor rule. Our experiments show that stereotyping significantly improves cases where using just the original method performs badly and that revaluation and switching fortify stereotyping against the potential risk of using an incorrect stereotype. Compactification shows good potential for improving efficiency, but is sometimes at risk of losing important observations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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