Safety Validation Pipeline

Language models can generate plausible but incorrect information (hallucinations), violate content policies, or produce responses that exceed authorized permissions. Mavaia's Safety Validation Pipeline addresses these risks through multi-stage validation that occurs after model inference but before response delivery. The pipeline operates on four principles: Fact Verification (checking factual claims against known sources), Source Grounding (ensuring claims are attributable to context or memory), Confidence Calibration (assessing model certainty), and Policy Enforcement (validating content and action constraints). Implementation spans five components: Factual Claim Extractor, Source Verification Engine, Confidence Calibrator, Policy Validator, and User Confirmation Interface for ambiguous cases.

Safety validation processes each model response through sequential checks. Factual Claim Extraction uses dependency parsing and entity recognition to identify verifiable claims (dates, names, numbers, causal relationships). Source Verification checks whether each claim is grounded in provided context (conversation history, retrieved memories, workspace data) or requires external verification. Confidence Calibration analyzes model output probabilities and linguistic hedging to assess certainty, flagging low-confidence responses for additional verification. Policy Validation checks generated actions against user permissions and content policies, requiring user confirmation for potentially sensitive operations. Hallucination Detection combines source grounding failures with low confidence scores to identify likely fabrications.

Safety pipeline evaluation showed 94% hallucination detection across 2,000 test cases with known ground truth. Source grounding achieved 89% accuracy in determining whether claims were attributable to provided context. Confidence calibration produced well-calibrated uncertainty estimates with 0.73 correlation between confidence scores and accuracy. Policy enforcement reached 97% constraint validation with 3% requiring user confirmation for ambiguous permissions. The pipeline added 80-150ms latency to response generation, with higher latency for responses containing many factual claims. False positive rate (incorrectly flagging correct information) remained below 5%, ensuring the system doesn't excessively block valid responses.

The Safety Validation Pipeline demonstrates that post-hoc validation can significantly reduce AI system risks without requiring model retraining. The 94% hallucination detection rate shows that source grounding combined with confidence calibration effectively identifies fabricated information. The 97% policy enforcement validates that action validation can prevent unauthorized operations. The 80-150ms latency is acceptable for the safety improvement provided. The architecture's strength lies in treating safety as a pipeline component rather than relying solely on model behavior. The 5% false positive rate indicates the system balances safety with usability, avoiding excessive blocking.

Current limitations include: (1) Factual claim extraction misses implicit claims or complex causal relationships, (2) Source verification limited to provided context without external fact-checking, (3) Confidence calibration relies on linguistic signals that sophisticated models can manipulate, (4) Policy validation uses rule-based checks that may not capture nuanced violations, (5) The system cannot detect subtle bias or framing issues that don't violate explicit policies, (6) Hallucination detection focused on factual claims, missing creative or subjective hallucinations, (7) User confirmation interface may create friction for legitimate edge case responses.

Mavaia's Safety Validation Pipeline provides multi-stage validation for model outputs, achieving 94% hallucination detection and 97% policy enforcement. The system demonstrates that architectural safety measures can significantly reduce AI risks through post-hoc validation. Future work will focus on external fact-checking integration, improved confidence calibration, enhanced policy understanding beyond rule-based matching, bias and framing detection, and refined user confirmation flows that minimize friction while maintaining safety.