Frameworks For Supporting LLM/Agentic Benchmarking

The current approach to benchmarking AI models has a serious problem: frontier labs keep building new models, setting up test harnesses, and running massive benchmarking suites just to show tiny improvements. This wastes enormous resources—we're essentially burning carbon for marginal confidence gains. Looking at recent benchmarks like Gemini, the inefficiency becomes obvious. The post argues we need better frameworks to test AI models without this wasteful cycle of constant retraining and retesting.