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
Checkpoint prediction and intelligent management have been recently proposed for reducing the number of coarse-grain checkpoints needed to achieve high performance through speculative execution. In this work, we take a closer look at various checkpoint prediction and management alternatives, comparing their performance and requirements as the scheduler window size increases. We also study a few additional design choices. The key contribution of this work is BranchTap, a novel checkpoint-aware speculation strategy that temporarily throttles speculation to reduce recovery cost while allowing speculation to proceed when it is likely to boost performance. BranchTap dynamically adapts to application behavior. We demonstrate that for a 1K-entry window processor with a FIFO of just four checkpoints, our adaptive speculation control mechanism leads to an average performance degradation of just 1.49% compared to a processor that has an infinite number of checkpoints. This represents an improvement of 28.3% over using just prediction-based checkpoint allocation. Average performance degradation without BranchTap is 2.08%. For the same configuration, BranchTap decreases the worst case deterioration from 8.99% to 5.64%.
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