LLMArena: Adversarial Benchmark-as-a-Service with Human-in-the-Loop Curation

A managed service that maintains a continuously evolving suite of adversarial evaluation tasks curated by domain experts (ML researchers, security researchers, domain specialists) who design, validate, and rotate benchmarks quarterly based on model performance plateaus. Labs submit model checkpoints; ArenaLLM runs them against the current task set, returns granular capability scores, and feeds hard-case data back to curators who design the next generation of tests. No static leaderboard—each lab gets private, comparative results against their own prior versions and anonymized peer baselines.