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Introducing correctover-patronus: 6-Dimensional Verification for Patronus AI

Correctover released correctover-patronus, an adapter that integrates its 87 deterministic verification rules as native evaluators within the Patronus AI framework. The tool adds six verification dimensionsβ€”structure, schema, identity, integrity, latency, and costβ€”and generates a recomputable proof hash for each verdict, enabling verifiable output assurance for LLM applications.

read2 min views1 publishedJul 1, 2026

LLM evaluation tools like Patronus AI excel at hallucination detection, toxicity checks, and semantic relevance. But they don't catch the structural failures:

These aren't hallucinations. They're verification failures.

correctover-patronus is an adapter that runs Correctover's 87 deterministic verification rules as native Patronus evaluators. Every verdict comes with a recomputable proof hash β€” meaning you can verify the verifier.

pip install correctover-patronus
Dimension What It Checks Example
Structure
Output format validity JSON parses correctly
Schema
Field presence & types Required fields exist
Identity
Semantic relevance to input Response addresses the question
Integrity
Forbidden pattern absence No Tracebacks or error messages
Latency
Response time budget Under 30s threshold
Cost
Token usage budget Under 10k token limit
from correctover_patronus import CorrectoverEvaluator, CorrectoverConfig

config = CorrectoverConfig(
    min_confidence=0.7,
    latency_rules={"max_ms": 5000},
    cost_rules={"max_tokens": 4000}
)

evaluator = CorrectoverEvaluator(config=config)
result = evaluator.evaluate(
    task_input="Summarize this article...",
    task_output="The article discusses...",
    task_context={"source": "article", "word_count": 1500}
)

print(f"Overall: {result.score:.2f} ({'PASS' if result.pass_ else 'FAIL'})")
print(f"Proof hash: {result.metadata['proof_hash']}")
for dim, info in result.metadata['dimensions'].items():
    print(f"  {dim}: {info['status']} (score={info['score']:.2f})")
python
from correctover_patronus import correctover_structure, correctover_integrity

is_valid = correctover_structure(task_output='{"key": "value"}')

is_clean = correctover_integrity(task_output="Result: 42")
python
from correctover_patronus import correctover_full

results = patronus.evaluate(
    evaluators=[correctover_full],
    dataset=my_dataset,
    experiment_name="correctover-benchmark"
)

Every evaluation produces a proof_hash

in the metadata. This hash covers:

You can re-run the same verification and get the same hash. No black boxes.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Patronus AI   │────>β”‚  correctover-patronus β”‚
β”‚   Framework     β”‚     β”‚  (this adapter)       β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚     Correctover SDK         β”‚
                    β”‚  (87 rules, 6 dimensions)   β”‚
                    β”‚  P50 verification: 22us     β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚   Verification Request      β”‚
                    β”‚   -> Verdict + Proof Hash    β”‚
                    β”‚   -> Metadata + Tags         β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

*Failover β‰  Correctover.*β„’

*Correctover verifies. Patronus evaluates. Together: complete output assurance.

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