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
Smooth entropies are a tool for quantifying resource trade-offs in (quantum) \ninformation theory and cryptography. In typical bi- and multi-partite problems, \nhowever, some of the sub-systems are often left unchanged and this is not \nreflected by the standard smoothing of information measures over a ball of \nclose states. We propose to smooth instead only over a ball of close states \nwhich also have some of the reduced states on the relevant sub-systems fixed. \nThis partial smoothing of information measures naturally allows to give more \nrefined characterizations of various information-theoretic problems in the \none-shot setting. In particular, we immediately get asymptotic second-order \ncharacterizations for tasks such as privacy amplification against classical \nside information or classical state splitting. For quantum problems like state \nmerging the general resource trade-off is tightly characterized by partially \nsmoothed information measures as well. However, for quantum systems we can so \nfar only give the asymptotic first-order expansion of these quantities.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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