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
Approximate computing explores opportunities that emerge when applications can tolerate error or inexactness. These applications, which range from multimedia processing to machine learning, operate on inherently noisy and imprecise data. We can trade-off some loss in output value integrity for improved processor performance and energy-efficiency. As memory accesses consume substantial latency and energy, we explore load value approximation, a micro architectural technique to learn value patterns and generate approximations for the data. The processor uses these approximate data values to continue executing without incurring the high cost of accessing memory, removing load instructions from the critical path. Load value approximation can also inhibit approximated loads from accessing memory, resulting in energy savings. On a range of PARSEC workloads, we observe up to 28.6% speedup (8.5% on average) and 44.1% energy savings (12.6% on average), while maintaining low output error. By exploiting the approximate nature of applications, we draw closer to the ideal latency and energy of accessing memory.
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