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
Composition is something we take for granted in classical algorithms design, and in particular, we take it as a basic axiom that composing "efficient" algorithms should result in an "efficient" algorithm – even using this intuition to justify our definition of "efficient". Composing quantum algorithms is a much more subtle affair than composing classical algorithms. It has long been known that zero-error quantum algorithms do not compose, but it turns out that, using the right algorithmic lens, bounded-error quantum algorithms do. In fact, in the bounded-error setting, quantum algorithms can even avoid the log factor needed in composing bounded-error randomized algorithms that comes from amplifying the success probability via majority voting. In this article, we try to give some intuition for these results: why composing quantum algorithms is tricky, particularly in the zero-error setting, but why it nonetheless works better than classical composition in the bounded-error setting.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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