Why this work is in the frame
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Bibliographic record
Abstract
In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size <inline-formula><tex-math notation="LaTeX">$O(\log n)$</tex-math></inline-formula> to buckets of size <inline-formula><tex-math notation="LaTeX">$O(\log \log n)$</tex-math></inline-formula> by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash retains MinHash's features of streaming updates, unions, and cardinality estimation. For a additive approximation error <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> on a Jaccard index <inline-formula><tex-math notation="LaTeX">$ t$</tex-math></inline-formula> , given a random oracle, HyperMinHash needs <inline-formula><tex-math notation="LaTeX">$O\left(\epsilon ^{-2} \left(\log \log n + \log \frac{1}{ \epsilon } \right)\right)$</tex-math></inline-formula> space. HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on the order of <inline-formula><tex-math notation="LaTeX">$10^{19}$</tex-math></inline-formula> with relative error of around 10 percent using 2MiB of memory; MinHash can only estimate Jaccard indices for cardinalities of <inline-formula><tex-math notation="LaTeX">$10^{10}$</tex-math></inline-formula> with the same memory consumption.
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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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