Synthesis-powered optimization of smart contracts via data type refactoring
Why this work is in the frame
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Bibliographic record
Abstract
Since executing a smart contract on the Ethereum blockchain costs money (measured in gas ), smart contract developers spend significant effort in reducing gas usage. In this paper, we propose a new technique for reducing the gas usage of smart contracts by changing the underlying data layout. Given a smart contract P and a type-level transformation, our method automatically synthesizes a new contract P ′ that is functionally equivalent to P . Our approach provides a convenient DSL for expressing data type refactorings and employs program synthesis to generate the new version of the contract. We have implemented our approach in a tool called Solidare and demonstrate its capabilities on real-world smart contracts from Etherscan and GasStation. In particular, we show that our approach is effective at automating the desired data layout transformation and that it is useful for reducing gas usage of smart contracts that use rich data structures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.003 |
| 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