L

llm-evaluation

Model Verification Layer is a modular system for evaluating, comparing, and validating the behavior of large language models using structured benchmarks, logic consistency checks, cross-model consensus analysis, and policy-aware constraints. It provides a transparent framework for understanding how different AI models perform under identical conditions, enabling more reliable model selection, safer deployment, and user-adaptive decision making. https://roxanneardary.com/model-verification-layer/

Most LLM reasoning benchmarks come from Western math, English logic, or code. Bazi-Bench tests multi-step rule-following inference in a different formal system: traditional Chinese Ba Zi. Frozen tables, Python reference impl, gold CoT cases, all mechanically verifiable.