VLR-Recurrence-Verified

VLR-Recurrence-Verified is a synthetic-data construction release for studying evidence-convergent program repair. It operationalizes a protected partial order: a candidate is positive only when it preserves every already-satisfied obligation and strictly improves at least one unresolved obligation.

Scale

Split	Tasks	Families	Transitions	Balanced pairs	Certified finals
Train	3,500	28	12,250	49,000	3,500
Validation	750	10	2,623	10,492	750
Test	750	12	2,623	10,492	750
Total	5,000	50	17,496	69,984	5,000

The release contains 224,952 records across the same seven views and 21 files as the earlier version. These records derive from 5,000 independent tasks; view rows that share a task or transition are not independent observations.

Construction and labels

Each task defines 16–31 protected executable obligations and a deterministic 2–5 step canonical repair path. The external verifier executes the incumbent, accepted candidate and every negative against the same immutable obligations. Only componentwise non-regression with at least one strict improvement receives a positive label, and only an all-zero EvidenceRank receives certification. The 50 semantic families span additive corrections, multiplicative gains and bit-mask repairs over integer, text, sequence and structured-record programs.

Balanced preference materialization includes exactly one example from each negative class per transition:

• equal_rank: the artifact changes but protected evidence does not;
• protected_regression: at least one passing obligation breaks and none improves;
• pareto_incomparable: improvements and regressions coexist without aggregate gain;
• aggregate_trap: aggregate failures decrease while a protected obligation regresses.

All v4 hard negatives are structural parameter regressions over protected input regions. The audit rejects candidates containing the legacy single-hidden-input exception pattern.

Leakage controls

Splits are assigned before view materialization at task and semantic-family level. Validation and test semantic families never appear in optimization. Cross-split overlap is zero for task IDs, family IDs, exact accepted artifacts and identifier/literal-normalized accepted Python ASTs. Model-visible prompts contain sanitized failure counts and opaque obligation IDs, never protected inputs, expected hidden outputs or full audit receipts.

Recommended post-training

Use accepted_action_train, certified_generation_train and preference_balanced_train. Keep the synchronized primary, exhaustive, trajectory and STOP views out of ordinary loss unless an explicitly reported ablation changes that contract. The supplied trainer uses the shared Hugging Face engine with a VLR-specific length-normalized protected-order objective, inverse-frequency route balancing and stronger regression-boundary weights.

Do not infer deployment correctness from model likelihood or a generated STOP token. The trained model proposes candidates; an independent verifier retains exclusive commit, rollback, certification and stopping authority.

Evaluation protocol

Checkpoint selection uses the family-OOD validation split. Primary outcomes are externally executed strict-progress rate, protected-regression rate and zero-rank completion. The release gate evaluates one terminal transition per independent task, reports Wilson 95% intervals, compares the frozen base and adapter with an exact paired McNemar test, and requires no degradation on the independent code, mathematics and quantum retention panel. The sealed test split may be evaluated once only after configuration freeze.

For a data-construction-method paper, report at least the following ablations under identical models and training budgets: accepted-action SFT only; preference without protected hard negatives; unbalanced joint optimization; the complete v4 objective; and data-scale checkpoints at 10%, 25%, 50% and 100%. Report every seed and confidence interval rather than selecting the best seed or test epoch.

Limitations

The task domain is deterministic Python function repair with synthetic, machine-verifiable specifications. Semantic-family OOD evaluation is stronger than random row splitting but does not establish repository-scale or cross-language generalization. The evidence labels certify the generated data; they do not make an unverified model generation correct at runtime.