PROOF / FIELDCODE VERIFICATION NOTES

AI large scale code verification

AI code verification.
Plan for scale.

A practical field guide to verifying AI-generated code across a growing repository. Choose checks that produce evidence your team can review.

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verification-plan.md
# A change is ready when the evidence is.

scope   payment service / changed paths
risk    authorization + money movement

01   State the invariantrequired
02   Test the failure pathrequired
03   Review dependency contextrequired
04   Attach reproducible evidencerequired

$ human_review --with-context
→ a decision someone can explain
ILLUSTRATIVE PLAN · No scan has been run
01 / Separate review from evaluation 02 / Match the method to the failure 03 / Follow the primary research

Start with the job

What are you trying to verify?

Compare the methods ↗
01 / APPLICATION TEAMS

An AI-assisted change

Check behavior, security and interactions beyond the diff before you merge a pull request.

Read the workflow ↗02 / EVALUATION TEAMS

Many generated programs

Choose execution limits and judges for code evaluation workloads. A pass depends on the oracle.

Explore ScaleBox ↗03 / CRITICAL LOGIC

A precise property

Express preconditions, postconditions and invariants when a proof-oriented approach fits the problem.

Understand formal methods ↗

Research in context

Scaling execution starts with defining a correct answer.

ScaleBox studies code evaluation infrastructure for language-model training. Its architecture separates execution from result verification, including special judges for tasks with more than one acceptable output.

That is a different job from deciding whether a pull request belongs in your application.

Read the research notes ↗
ScaleBox architecture showing request routing, extraction, dispatch, execution and verification
Zheng et al., ACL 2026, ScaleBox. © 2026 ACL, CC BY 4.0. Figure cropped from the published PDF; content unchanged.

A usable review loop

Keep the evidence close to the change.

DEFINE

Write the property.

“An expired token cannot change a billing address” gives the reviewer something concrete to check.

EXERCISE

Try to break it.

Cover rejection paths, boundary values and the callers that depend on the changed behavior.

DECIDE

Record the evidence.

Attach commands, results and known gaps. Require an owner to accept the remaining risk.

Review the findings

Find the alert that needs a decision.

Read the review loop ↗

GitHub’s code-scanning view lets a team narrow a finding list by tool, rule and severity. Inspect the relevant branch and analysis context before deciding what a finding means for a change.

GitHub documentation example showing code-scanning filters for language, tool, rule and severity
GitHub Docs contributors. Official documentation example, CC BY 4.0 (license text). Unmodified screenshot. All alert counts and statuses are illustrative documentation data, not Proof Field scan results or statistics.

The screenshot is a published GitHub documentation example. This website does not scan repositories or report alert counts.

Give the next change a clear path.

Choose your stack. Get a reviewable verification plan.

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