EvalResearchBench tests whether AI agents can build their own evaluators for recursive self-improvement
AG2AI · hf · 2026-10-24
AG2AI and researchers from Oregon State, UCSD, and Penn State introduce EvalResearchBench (ERB), a benchmark for autonomous evaluation research aimed at recursive self-improvement (RSI).
- Motivation: RSI relies on evaluation feedback to gauge progress, but rerunning complex benchmarks is costly and slow; human experts only cut costs by picking benchmark subsets.
- Setup: Given target materials, dev references, candidate APIs, and fixed time/API budgets, a "researcher" agent selects or synthesizes tasks, implements graders, and freezes an executable evaluator after pilot tests — spanning coding, co-work, and reasoning tasks.
- Scale: The study covers 9 researchers × 13 candidate models, comparing each frozen evaluator.
- Findings: Agents can automate benchmark design to lower evaluation costs, but generated evaluators still fall short of target agreement, with budget exhaustion and score dominance issues.
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