Socially Consistent Characters in Player-Specific Stories
Bibliographic record
Abstract
In the context of interactive, virtual experiences, the use of personality models to maintain consistent character behaviour is becoming more widespread in both industry and academia. Most current techniques, however, are limited in one of three ways: either they overly restrict user actions, have a high cost for creating varied content, or rely on a representation that prohibits conveying complex content to the user. Toward addressing these issues, we introduce Socially Consistent Role Passing, a mechanism for ensuring consistent character behaviour that leverages the design of PaSSAGE, an existing system for generating adaptive, interactive stories. While results from previous human user studies have shown that PaSSAGE improves the enjoyment of players with little gaming experience, we present results from a new study showing that PaSSAGE's adaptive stories, augmented with Socially Consistent Role Passing, improve the enjoyment of all players versus a set of fixed-structure alternatives.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".