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
Software instrumentation is a key technique in many stages of the development process. It is of particular importance for debugging embedded systems. Instrumented programs produce data traces which enable the developer to locate the origins of misbehaviours in the system under test. However, producing data traces incurs runtime overhead in the form of additional computation resources for capturing and copying the data. The instrumentation may therefore interfere with the system's timing and perturb its behavior. In the worst case, this perturbation leads to new system behaviours that prevent the developer from locating the original misbehaviours. In this work, we propose an instrumentation technique for applications with temporal constraints, specifically targetting background/foreground systems. Our framework permits reasoning about space and time for software instrumentations. In particular, we propose a definition for trace reliability, which enables us to instrument real-time applications which aggressively push their time budgets. Using the framework, we present a method with low perturbation by optimizing the number of insertion points and trace buffer size for code size and time budgets. Finally, we apply the theory to a concrete case study and instrument the OpenEC firmware for the keyboard controller of the One Laptop Per Child project.
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 | 0.000 | 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