Modular Verification of Distributed Systems with Grove
Bibliographic record
Abstract
Grove is a new framework for machine-checked verification of distributed systems. Grove focuses on modular verification. It enables developers to state and prove specifications for their components (e.g. an RPC library), and to use those specifications when proving the correctness of components that build on it (e.g. a key value service built on RPC). To enable modular specification and verification in a distributed systems, Grove uses the idea of ownership from separation logic. Using Grove, we built a verified unreliable RPC library, where we captured unreliability in the formal specification by using duplicable ownership. We also built a verified exactly-once RPC library, where we reasoned about ownership transfer from the client to server (and back) over an unreliable network by using the escrow pattern. Overall, we developed and verified an example system written in Go consisting of the RPC libraries, a sharded key-value store with support for dynamically adding new servers and rebalancing shards, a lock service, and a bank application that supports atomic transfers across accounts that live in different shards, built on top of these services. The key-value service scales well with the number of servers and the number of cores per server. The proofs are mechanized in the Coq proof assistant using the Iris library and Goose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".