Bibliographic record
Abstract
We consider the offline sorting buffers problem. Input to this problem is a sequence of requests, each specified by a point in a metric space. There is a “server” that moves from point to point to serve these requests. To serve a request, the server needs to visit the point corresponding to that request. The objective is to minimize the total distance travelled by the server in the metric space. In order to achieve this, the server is allowed to serve the requests in any order that requires to “buffer ” at most k requests at any time. Thus a valid reordering can serve a request only after serving all but k previous requests. In this paper, we consider this problem on a line metric which is motivated by its application to a widely studied disc scheduling problem. On a line metric with N uniformly spaced points, our algorithm yields the first constant-factor approximation and runs in quasi-polynomial time O(m · N · k O(log N) ) where m is the total number of requests. Our approach is based on a dynamic program that keeps track of the number of pending requests in each of O(log N) line segments that are geometrically increasing in length.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".