Dual Execution for On the Fly Fine Grained Execution Comparison
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
Execution comparison has many applications in debugging, malware analysis, software feature identification, and intrusion detection. Existing comparison techniques have various limitations. Some can only compare at the system event level and require executions to take the same input. Some require storing instruction traces that are very space-consuming and have difficulty dealing with non-determinism. In this paper, we propose a novel dual execution technique that allows on-the-fly comparison at the instruction level. Only differences between the executions are recorded. It allows executions to proceed in a coupled mode such that they share the same input sequence with the same timing, reducing nondeterminism. It also allows them to proceed in a decoupled mode such that the user can interact with each one differently. Decoupled executions can be recoupled to share the same future inputs and facilitate further comparison. We have implemented a prototype and applied it to identifying functional components for reuse, comparative debugging with new GDB primitives, and understanding real world regression failures. Our results show that dual execution is a critical enabling technique for execution comparison.
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 | 0.003 |
| 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 | 0.001 | 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