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
The end goal of failure diagnosis is to locate the root cause. Prior root cause localization approaches almost all rely on statistical analysis. This paper proposes taking a different approach based on the observation that if we model an execution as a totally ordered sequence of instructions, then the root cause can be identified by the first instruction where the failure execution deviates from the non-failure execution that has the longest instruction sequence prefix in common with that of the failure execution. Thus, root cause analysis is transformed into a principled search problem to identify the non-failure execution with the longest common prefix. We present Kairux, a tool that does just that. It is, in most cases, capable of pinpointing the root cause of a failure in a distributed system, in a fully automated way. Kairux uses tests from the system's rich unit test suite as building blocks to construct the non-failure execution that has the longest common prefix with the failure execution in order to locate the root cause. By evaluating Kairux on some of the most complex, real-world failures from HBase, HDFS, and ZooKeeper, we show that Kairux can accurately pinpoint each failure's respective root cause.
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 | 0.001 |
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