Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is available, revoking resource allocations to tasks in progress if new opportunities arise that those resources are better suited to handle. Our multiagent model assigns a task agent to each task that must be completed and a proxy agent to each resource that is available. Preemption occurs when a task agent approaches a proxy agent with a sufficiently compelling need that the proxy agent determines the newcomer derives more benefit from the proxy agent’s resource than the task agent currently using that resource. Task agents reason about which resources to request based on a learning of churn and congestion. We compare to a well-established multiagent resource allocation framework that permits preemption under more conservative assumptions and show through simulation that our model allows for improved allocations through more permissive preemption. In all, we offer a novel approach for multiagent resource allocation that is able to cope well with dynamic task arrivals.
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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 | 0.000 | 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