Concise descriptions of subsets of structured sets
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
We study the problem of economical representation of subsets of structured sets, that is, sets equipped with a set cover. Given a structured set U, and a language L whose expressions define subsets of U, the problem of Minimum Description Length in L (L-MDL) is: "given a subset V of U, find a shortest string in L that defines V".We show that the simple set cover is enough to model a number of realistic database structures. We focus on two important families: hierarchical and multidimensional organizations. The former is found in the context of semistructured data such as XML, the latter in the context of statistical and OLAP databases. In the case of general OLAP databases, data organization is a mixture of multidimensionality and hierarchy, which can also be viewed naturally as a structured set. We study the complexity of the L-MDL problem in several settings, and provide an efficient algorithm for the hierarchical case.Finally, we illustrate the application of the theory to summarization of large result sets, (multi) query optimization for ROLAP queries, and XML queries.
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