Understanding bloom filter intersection for lazy address-set disambiguation
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
A Bloom filter is a probabilistic bit-array-based set representation that has recently been applied to address-set disambiguation in systems that ease the burden of parallel programming. However, many of these systems intersect the Bloom filter bit-arrays to approximate address-set intersection and decide set disjointness. This is in contrast with the conventional and well-studied approach of making individual membership queries into the Bloom filter. In this paper we present much-needed probabilistic models for the unconventional application of testing set disjointness using Bloom filters. Consequently, we demonstrate that intersecting Bloom filters requires substantially larger bit-arrays to provide the same probability of false set-overlap as querying into the bit-array. For when intersection is unavoidable, we prove that partitioned Bloom filters require less space than unpartitioned. Finally, we show that for Bloom filters with a single hash function, surprisingly, intersection and querying share the same probability of false set-overlap.
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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 | 0.000 |
| 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