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
To this day, debugging support for the DEVS formalism has been provided, at best, in an ad-hoc way. The intricacies of dealing with the interplay of different notions of (simulated) time, formalism semantics, and user input have not been thoroughly investigated. This paper presents a visual modeling, simulation, and debugging environment for Parallel DEVS, which builds on a theoretical foundation for debugging DEVS models. We take inspiration from both code debugging and the simulation world to model our environment; we transpose a set of useful code debugging concepts onto Parallel DEVS, and combine those with simulation-specific operations, such as as-fast-as-possible simulation and (scaled) real-time execution. Apart from these common debugging operations, we introduce new features to the debugging of Parallel DEVS models, such as “god events,” which can alter the model state during simulation, and reversible debugging, which allows one to go back in time. To achieve this, the PythonPDEVS simulator is deconstructed and reconstructed: the modal part of the simulator–debugger, as well as the debugging operations, are modeled using the Statecharts formalism. These models are combined, resulting in a model of the timed, reactive behavior of a debuggable simulator for Parallel DEVS. The code for the simulator is automatically synthesized from this model. To improve usability, we combine the simulator with a visual modeling environment, allowing for visual and interactive live debugging.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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