A categorization theorem on suffix arrays with applications to space efficient text indexes
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we design succinct index structures for a text string T of n binary symbols to support efficient searching of a pattern P of length m. Motivated by the fact that the standard representation of suffix arrays uses n lg n bits which is more than the theoretical minimum, we present a theorem that characterizes a permutation as the suffix array of a binary string. Based on the theorem, we design a succinct representation of suffix arrays of binary strings that uses n + o(n) bits, which is the theoretical minimum plus a lower order term, and answers existential and cardinality queries in O(m) time without storing the raw text. With 2n+o(n) bits, we can list pattern occurrences in O(m + occ lg n) time in the general case, and for long patterns, when m = Ω(lg1+∈n), we answer such listing queries in O(m + occ) time. We also present another implementation that uses O(n) bits and supports pattern searching in O(m + occ lgλn) time for any fixed λ such that 0
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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