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

The Problem LLM evaluation tools like Patronus AI excel at hallucination detection, toxicity checks, and semantic relevance. But they don't catch the structural failures: A JSON response missing required fields A function call with malformed parameters Output that violates schema constraints Latency budget overruns silently degrading UX Cost explosions from runaway token usage These aren't hallucinations. They're verification failures. The Solution 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 — meaning you can verify the verifier. pip install correctover-patronus The 6 Dimensions 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 Usage Full 6-Dimension Verification 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 : . 2 f } ( { ' 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 ' ] : . 2 f } ) " ) Individual Dimensions from correctover_patronus import correctover_structure , correctover_inte