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
Fuzz testing enables the discovery of vulnerabilities, ideally providing details to help mitigate such issues and measure the extent to which code branches were tested. The inputs used during fuzz testing can usually be specified using a grammar. The complexity of the grammar of the input can be reflected in the different code branches covered during testing. The hypothesis of this research is that coverage of the input grammar can provide some prediction of the code coverage achieved during fuzz testing. In this work, grammar coverage is compared to code coverage using the LibAFL framework and the Knot DNS server as the target, comparing the well known AFL feedback against nine other feedback algorithms.A key to this approach is using the input language to build feature vectors which represent the structure and abstract the values of the input. This research demonstrated that grammar-based coverage is useful in the absence of execution data, both for fuzzing feedback and for identifying potentially erroneous output data.
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 | 0.001 |
| 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 | 0.000 |
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