Muhammad Maaz

We redesigned llm-eval-kit into a deterministic, zero-network evaluation pipeline built for reliable continuous evaluation in CI/CD environments. The new architecture introduces a decoupled criteria registry, fail-fast orchestration, hybrid semantic and symbolic verification, and graceful handling of inapplicable evaluation criteria. The implementation reached 93% test coverage across 88 tests, with cross-version CI validation from Python 3.9 to 3.12. The work demonstrates how carefully defined evaluation contracts and offline heuristics can provide reproducible, privacy-preserving model assessment without relying on external LLM-as-a-judge APIs.