Towards Verifiable-by-Design Smart Contracts: A Declarative Limit Order Books Implementation
Bibliographic record
Abstract
We present a declarative approach to on-chain limit order books (LOBs) that prioritizes formal verification over raw throughput. Unlike automated market makers, LOBs offer granular control and capital efficiency but are difficult to verify when implemented imperatively in Solidity. Using Pint, a declarative domain-specific language, we encode LOB matching logic, price-time priority, partial fills, and asset conservation, as first-order constraints. Off-chain solvers compute valid state transitions, while the blockchain performs lightweight constraint verification. We implement eight LOB predicates and evaluate performance using real-world transaction traces. Our declarative LOBs achieve 141 predicates/s for simple operations and 11 predicates/s for complex settlement with 1,000 accounts. Performance correlates strongly with state access patterns rather than constraint complexity. Critically, our approach eliminates verification challenges that make imperative smart contracts hard to formally verify, such as unbounded loops, recursion, cross-contract/function calls, and complex control flow. This enables correctness-by-construction through constraint satisfaction, removing the need to prove implementation conformance to specifications. This work demonstrates the first practical evidence that declarative LOBs achieve reasonable performance while providing superior verification guarantees for DeFi protocols.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".