A Framework for Non-Interactive Instance-Dependent Commitment Schemes (NIC).
Bibliographic record
Abstract
Zero-knowledge protocols are often studied through specific problems, like GRAPH-ISOMORPHISM. In many cases this approach prevents an important level of abstraction and leads to limited results, whereas in fact the constructions apply to a wide variety of problems. We propose to address this issue with a formal framework of non-interactive instance-dependent commitment schemes (NIC). We define NIC in both the perfect, statistical, and computational settings, and formally characterize problems admitting NIC in all of these settings. We also prove other useful lemmas such as closure properties. Consequently, results that previously applied only to specific problems are now strengthened by our framework to apply to classes of problems. By providing formal yet intuitive tools, our framework facilitates the construction of zero-knowledge protocols for a wide variety of problems, in various settings, without the need to refer to a specific problem. Our results are unconditional.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".