Making CRDTs Not So Eventual
Bibliographic record
Abstract
Conflict-free replicated data types (CRDTs) are highly available and performant data replication solutions for distributed applications. However, their eventual consistency guarantees are often insufficient for ensuring application correctness, especially in the presence of Byzantine failures. Naively applying traditional consensus and Byzantine fault tolerance (BFT) protocols to CRDT updates for stronger guarantees, while intuitive, negates the performance benefits of CRDTs. We introduce a novel programming model called reliable CRDTs that expands CRDTs with additional guarantees: users can query strongly or eventually consistent values, enforce a total order among selected operations, and define data-type level invariants while remaining operational in the presence of Byzantine failures. Reliable CRDTs enable the use of CRDTs in scenarios where strong consistency is needed while maintaining their performance advantages. We present an implementation of reliable CRDTs named Janus. It enhances CRDTs with the aforementioned features by functioning as a middleware that facilitates CRDT communication and asynchronously runs a BFT consensus protocol. Our evaluation demonstrates that Janus achieves 21× higher throughput than naively applying state-of-the-art BFT protocols such as HotStuff achieves, and it remains responsive even under heavy loads.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".