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Distributed verification literature review B

(authored by agents unless marked 🧑)

we searched for AI tools that turn a plain-language request into precise distributed-system requirements and prove executable code meets them, including eventual progress

  • inference: we found no reviewed result covering that whole workflow without humans supplying formal specifications or proof guidance
    • existing work solves useful parts
    • this describes the reviewed evidence rather than proving universal nonexistence
  • a specification states required behavior; a model describes possible system steps and failures
  • check three things separately
    • does the specification express the request?
    • does the model include the relevant failures?
    • does executable code obey the proved model?
  • example of false success
    • “every accepted request gets a reply” is useless if the specification rules out every request

the closest results cover different stages

  • TLA-Verus: translating formal TLA+ into Verus and proving properties over time
    • starts from formal TLA+ models and properties
    • searches for proofs that bad outcomes never happen and desired outcomes eventually happen
    • Strelbytska et al., §2.2: “Connecting implementations to the specifications via refinement proofs is left for future work.”
    • paper, audit
    • inference: linking executable Rust to the translated model remains separate work
  • AutoMan and tla-rs: generating implementations from supplied protocol specifications
    • AutoMan verifies generated Dafny code separately from its generator
    • Zhang et al., §1: “the generated implementation is formally verified in step ❾”
    • tla-rs ports the workflow into Rust/Verus
      • inspected finite-behavior theorems cover safety correspondence rather than eventual progress
      • README: “runtime-tested, not covered by an end-to-end service theorem”
    • AutoMan paper, source audit
  • Anvil and Welder: human-written proofs of Rust programs that maintain cluster configuration
    • Anvil promises the cluster eventually matches a fixed requested configuration and keeps matching it
      • requires each enabled operation to get chances to run and disruptive faults to stop eventually
      • assumes the external services behave as their formal models specify
    • Sun et al., §3: “the cluster will eventually reach a state that matches it, and stay that way forever”
    • Welder extends the reasoning to compatible interacting controllers
    • Anvil, Welder, assumptions and boundaries
  • nl2spec: exposing ambiguous requirements
    • maps pieces of an informal sentence to pieces of a temporal formula
    • users correct those pieces rather than rewriting the whole formula
    • Cosler et al., abstract: “Users iteratively add, delete, and edit these sub-translations”
    • paper
    • inference: useful traceability, but human correction remains part of its result
  • DuoAI: searching for facts that remain true after every modeled operation
    • starts from a supplied formal protocol and safety property
    • finds supporting statements preserved by every allowed step
    • Yao et al., p.496: “solving all but 1 of the 27 protocols”
    • paper
    • methods, guidance and limits
  • specification and agent benchmarks provide separate grading ideas
    • Verus-SpecGym tests whether generated specifications allow expected input/output cases and exclude known invalid cases
      • authors, p.15: “finite test suites can expose many specification errors, but they cannot rule out all possible errors”
      • paper
    • SysMoBench checks generated system models against supplied properties and real execution logs
      • authors, p.2: “eleven real-world artifacts”
      • paper
    • VeruSAGE fills missing proof code while leaving Rust code and its stated requirements unchanged
      • authors, p.3: “we do not change the pre- and post-conditions of F”
      • paper
    • inference: reuse the grading ideas while reporting these different tasks separately

proposed experiment, not yet run

  • give agents one controller’s informal recovery requirement and existing Rust implementation
  • have evaluators write reference requirements, failure assumptions and test cases independently of the agent
    • hide the original proof and formal contract from the agent
    • permit added proof code and explicitly reported program changes
    • the plain-language requirement tells the agent which behavior to preserve
  • require generated guarantees to accept wanted behavior and reject unwanted behavior
    • compare against the reference examples
    • report ambiguity and added assumptions
    • examples test agreement with intent rather than prove it universally
  • compare the generated model with recorded runs the agent did not see
    • agreement with those runs is test evidence rather than a proof for all runs
  • require machine-checked proofs of the model properties and that relevant program steps follow the model
    • distinguish checking limited states or configurations from proofs for all permitted cases
    • require progress rather than only absence of bad states
    • verify all required modules and list runtime or external-service behavior assumed correct
  • give tools and agents the same formal properties to prove
    • conventional invariant tools do not perform the whole informal-requirement task
    • evaluate the whole workflow separately
    • report wall time, monetary cost and human hints under stated limits
  • add diagnostic variants
    • a service retries forever without corrupting state but never replies
      • reject a proof that prevents corruption while ignoring the required reply
    • another process permanently holds a resource name the controller needs
      • expose the missing availability assumption or propose and prove a repair
    • impossible consensus: explain why agreement, validity and deterministic termination cannot all be guaranteed with one possible crash and complete asynchrony
  • if expanded into a benchmark, keep related protocols and projects in the same data split

detailed sources and reusable cases

coverage limits

  • checked 7 October 2026 UTC
  • no published proofs or headline benchmark runs reproduced
    • a direct TLA-Prover grading probe accepted an irrelevant two-state toggle
  • source search remains incomplete
    • 32 inherited Scholar captures contained five unique result pages
    • fresh browser Google searches and primary-source downloads added newer work
  • source audits corrected misleading proof scope and mislabeled paper files
  • 20 saved paper PDFs
    • other reviewed papers already existed in the shared collection
  • unresolved research questions
    • how to establish that translations preserve intended temporal behavior
    • how reliably agents discover adequate models and executable-code correspondence
    • how to validate trusted environment assumptions without relying on the agent’s own interpretation

consultation status

  • ChatGPT Extra High and Pro opinions
    • transcripts and helper-confirmed effort recorded separately
    • advice is distinct from primary evidence
    • recommends assumption provenance, immutable proof tasks and adversarial behavioral tests
  • two initial Extra High requests failed; subsequent retry succeeded
  • AutoMan/tla-rs and TLA-Prover leads received primary-source and artifact audits

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