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
In this paper, the Maximum Reward Collection Problem (MRCP) in uncertain environments is investigated where multiple agents cooperate to maximize the total reward collected from a set of moving targets in the mission space with unknown arrival times, trajectories and dynamics. The reward with respect to each of the targets has a time discounting value and can be collected only if a cluster of agents with proper number of elements visits the targets. Meanwhile, in each cluster, it is assumed that agents are able to extract a larger fraction of reward when their configuration in the cluster is close to specific configuration around the respective target. The inherited uncertainty in the environment and the dynamic clustering factor render the one-shot optimization in MRCP rather impractical. Therefore, a Cooperative Receding Horizon (CRH) controller is utilized toward maximizing the collected reward and based on the prediction of the future positions of targets with the given limited information. Some analytical aspects of problem is discussed and the effectiveness and advantages of the proposed algorithm is demonstrated via numerical simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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