On the optimal search problem: the case when the target distribution is unknown
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 consider the problem of searching for an object in a set of N locations (or bins) (C/sub 1/,...,C/sub N/). The probability of the object being in the location C/sub i/ is p(i). Also, the probability of locating the object in the bin within a specified time, given that it is in the bin, is given by a function called the detection function. This is typically specified by an exponential function. The intention is to allocate the available resources so as to maximize the probability of locating the object. This problem has applications in searching large databases and in developing various military and strategic policies. All of the research done in this area has assumed the knowledge of the {p(i)}-the target distribution. We consider the problem of obtaining error bounds and estimating the target distribution. To our knowledge these are the first available results in this area, and are particularly interesting because the target distribution, in itself, is unobservable.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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