Efficient Consistency Proofs for Generalized Queries on a Committed Database.
Bibliographic record
Abstract
A consistent query protocol (CQP) allows a database owner to publish a very short string c which commits her and everybody else to a particular database D, so that any copy of the database can later be used to answer queries and give short proofs that the answers are consistent with the commitment c. Here commits means that there is at most one database D that anybody can find (in polynomial time) which is consistent with c. (Unlike in some previous work, this strong guarantee holds even for owners who try to cheat while creating c.) Efficient CQPs for membership and one-dimensional range queries are known [5, 17, 22]: given a query pair a, b ∈ R, the server answers with all the keys in the database which lie in the interval [a, b] and a proof that the answer is correct. This paper explores CQPs for more general types of databases. We put forward a general technique for constructing CQPs for any type of query, assuming the existence of a data structure/algorithm with certain inherent robustness properties that we define (called a data robust algorithm). We illustrate our technique by constructing an efficient protocol for orthogonal range queries, where the database keys are points in R d and a query asks for all keys in a rectangle [a1, b1] ×... × [ad, bd]. Our data-robust algorithm is within a O(log N) factor of the best known standard data structure (a range tree, due to
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".