A heuristic method to solve an assignment problem using a random walk approximation
Why this work is in the frame
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Bibliographic record
Abstract
Over the past years, Robotic Process Automation (RPA) has emerged as a significant tool to enhance productivity across various industries by automating repetitive tasks performed on computer user interfaces, thereby reducing error rates. In this paper, the RPA problem is addressed as an unbounded assignment problem. Specifically, the objective of the problem is to assign robots to transactions that must be completed at specific time periods, while minimizing the total number of robots required. The problem is solved using a bipartite graph representation and a random walk approximation, which defines an ordering of the transactions and periods in order to determine a valid assignment. The heuristic is evaluated on a real data set from a financial institution and compared to previous results obtained on generated data. The results obtained with the random walk approximation heuristics on the real data set is optimal in terms of number of robots.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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