Stanford paper simulates AI eval ecosystem with LLM agents: holdout design tradeoffs
sanmikoyejo · x · 2026-10-11
Stanford's The AI Evaluation Ecosystem (arXiv:2610.09296, 70pp) builds a simulation combining rule-based market dynamics with LLM-driven strategic actors (GABM) across a six-dimensional capability space. Case study on benchmark holdout design: private holdouts shrink the benchmark-vs-user-satisfaction gap on most benchmarks but widen it on a few, depending on holdout weight allocation. To be presented at the AIMS workshop at COLM 2026.
Related event: Stanford Paper Models the AI Evaluation Ecosystem with Generative Agents(2 posts)→
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