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
Bugs in concurrent programs are extremely difficult to find and fix during testing. In this paper, we propose Kivati, which can efficiently detect and prevent atomicity violation bugs. Kivati imposes an average run-time overhead of 19%, which makes it practical to deploy on software in production environments. The key attribute that allows Kivati to impose this low overhead is its use of hardware watchpoints, which can be found on most commodity processors. Kivati combines watchpoints with a simple static analysis that annotates regions of codes that likely need to be executed atomically. The watchpoints are then used to monitor these regions for interleaving accesses that may lead to an atomicity violation. When an atomicity violation is detected, Kivati dynamically reorders the access to prevent the violation from occurring. Kivati can be run in prevention mode, which optimizes for performance, or in bug-finding mode, which trades some performance for an enhanced ability to find bugs.
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