Refereed Delegation of Computation Using Smart Contracts
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
Outsourcing computation enables a weak client to expand its computational power as the need arises. A basic requirement of outsourcing computation is the guarantee that the computation result is correct. Cryptographic solutions that provide verifiability for the computation result when the computation is outsourced to a single server, are complex and fragile. We consider the intuitive approach of verifiable computation, called verifiable computation by replication, when the computation is replicated on multiple servers, and a referee decides the result of the final computation using the outputs of all servers. We consider the case when a smart contact is used as the referee. We propose a security model in the Universal Composability (UC) framework of Canetti, and design a 2-server and an n-server protocol with proved security in our model. Our protocols build on the Refereed Delegation of Computation (RDoC) framework of Canetti, Riva, and Rothblum, underline the challenges of using a smart contract as a referee, and address those challenges in the designed protocols. We give the efficiency analysis of the protocols, provide a proof of concept implementation for our protocols using Ethereum smart contact, and give concrete cost values for an example computation.
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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 | 0.001 |
| 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