Predicting UNIX Command Lines: Adjusting to User Patterns
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
As every user has his own idiosyncrasies and preferences, an interface that is honed for one user may be problematic for another. To accommodate a diverse range of users, many computer applications therefore include an interface that can be customized --- e.g., by adjusting parameters, or defining macros. This allows each user to have his "own" version of the interface, honed to his specific preferences. However, most such interfaces require the user to perform this customization by hand --- a tedious process that requires the user to be aware of his personal preferences. We are therefore exploring adaptive interfaces, that can autonomously determine the user's preference, and adjust the interface appropriately. This paper describes such an adaptive system --- here a UNIX- shell that can predict the user's next command, and then use this prediction to simplify the user's future interactions. We present a relatively simple model here, then explore a variety of technique...
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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 | 0.000 | 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