Enterprise decision-maker briefing on MTCP as AI assurance system. Audience: CTOs, AI governance leads, procurement teams, risk officers.
Multi-Turn Constraint Persistence (MTCP) is a black-box evaluation framework that measures whether large language models maintain corrected constraints during interaction. Standard benchmarks test single-prompt compliance. MTCP evaluates behavioural durability across conversation turns — measuring how models respond after correction. 32 frontier models evaluated. 181,448 probe interactions.
MTCP provides formal evaluation reports, Release Decision Pack, and assurance certificates suitable for procurement documentation, risk assessment, and compliance frameworks.
Standard benchmarks (HumanEval, MMLU, MT-Bench) measure whether models follow instructions at one moment. They do not measure whether models maintain corrected behaviour during interaction. Production deployments require behavioural reliability after correction, not just initial compliance.
Proportion of probes where model maintained corrected constraints. BIS ≥90% = grade A.
Behavioural consistency across temperature variation. TSI >95 = highly stable.
Performance gap between primary and concealed control probes. CPD <-40 = high exposure risk.
Evaluate candidate models under MTCP. Compare BIS, TSI, CPD across vendors. Select model with strongest constraint persistence. Attach MTCP certificate to procurement documentation.
Run MTCP evaluation on deployed model. Generate risk metrics. Compare against industry standard. Establish minimum BIS threshold policy.
Set minimum BIS threshold (e.g. ≥85% for production). Gate deployment on MTCP evaluation pass. Re-evaluate after model updates. Maintain audit trail of evaluations.
Submit MTCP certificate as evidence. Provide Release Decision Pack and deployment verdict. Show evaluation methodology (DOI: 10.17605/OSF.IO/DXGK5). Demonstrate third-party evaluation.
| System | Evaluation Type | Multi-Turn | Constraint Persistence |
|---|---|---|---|
| MTCP | Constraint persistence | ✓ Yes | ✓ Enforced explicitly |
| HumanEval | Code generation (single turn) | ✗ No | ✗ No |
| MMLU | Knowledge retrieval (single turn) | ✗ No | ✗ No |
| MT-Bench | Multi-turn conversation | ✓ Yes | ✗ Not enforced |
| OpenAI Evals | Custom evaluation framework | Varies | ✗ No Release Decision Pack |
MTCP requires only API access. No model weights, training data, vendor cooperation, or internal access required.
DOI: 10.17605/OSF.IO/DXGK5 · Dataset: HuggingFace (mtcp-boundary-500) · Author: A. Abby · 2026 · Methodology fully documented · Results publicly available · Independent validation possible
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