Towards Automated Benchmark Support for Multi-Blockchain\n Interoperability-Facilitating Platforms
Bibliographic record
Abstract
Since the introduction of the first Bitcoin blockchain in 2008, different\ndecentralized blockchain systems such as Ethereum, Hyperledger Fabric, and\nCorda, have emerged with public and private accessibility. It has been widely\nacknowledged that no single blockchain network will fit all use cases. As a\nresult, we have observed the increasing popularity of multi-blockchain\necosystem in which customers will move toward different blockchains based on\ntheir particular requirements. Hence, the efficiency and security requirements\nof interactions among these heterogeneous blockchains become critical. In\nrealization of this multi-blockchain paradigm, initiatives in building\nInteroperability-Facilitating Platforms (IFPs) that aim at bridging different\nblockchains (a.k.a. blockchain interoperability) have come to the fore. Despite\ncurrent efforts, it is extremely difficult for blockchain customers\n(organizations, governments, companies) to understand the trade-offs between\ndifferent IFPs and their suitability for different application domains before\nadoption. A key reason is due to a lack of fundamental and systematic\napproaches to assess the variables among different IFPs. To fill this gap,\ndeveloping new IFP requirements specification and open-source benchmark tools\nto advance research in distributed, multi-blockchain interoperability, with\nemphasis on IFP performance and security challenges are required. In this\ndocument, we outline a research proposal study to the community to realize this\ngap.\n
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".