Towards a Distributed Plan Execution Monitoring Framework
Why this work is in the frame
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Bibliographic record
Abstract
Distributed monitoring is challenging yet essential in order to address scalability issues observed in the context of large-scale plan execution. A formal framework can be very helpful in analyzing and reasoning about plan spec- ification, execution, and monitoring. In this paper, we elaborate on a distributed monitoring calculus framework that allows specifying and executing plans for multi-agent systems in a distributed environment. The framework allows taking into account a highly dynamic and uncertain environment that can be a contributor to the changing conditions possibly disrupting and causing the plan to fail. Furthermore, the calculus provides sound foundations for designing and evaluating monitoring algorithms and protocols. In order to achieve effective monitoring, we propose an automata- based approach, inspired by runtime security verification research initiatives. The proposed automata allow enforcing monitoring properties while the given plan is executed at the agent's side.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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