April 2026 performance snapshot for kaito-project/kaito: Implemented a centralized model catalog with vLLM parameters and inference runtime integration, hardened preset and vLLM inference pathways with YAML-based metadata, and introduced workspace governance to reject non-preset model inferences. Added an MT-Bench evaluation framework and expanded the built-in model catalog to enable standardized benchmarking across deployed LLMs. Fixed an inference issue for phi-4-mini-instruct by removing trust_remote_code and enabling native execution with HF transformer runtime, supplemented by unit and end-to-end tests. Upgraded CI/CD with Kubernetes 1.33.8 to improve stability and feature access. Outcome: more reliable, scalable model deployments, faster inference, and measurable performance insights for business decisions.