研究发现小模型按评分标准批改试卷和大模型一样可靠，评分标准比模型智能更重要。
研究表明，小语言模型在明确评分标准下批改开放式考试答案的可靠性与昂贵模型相当。研究测试了来自两个模型家族的六种成本高效模型配置，每个配置回答24个问题并对每份答案评分三次，共产生3456个评分。答案身份解释了95.6%的评分差异，而评分者身份仅解释0.2%的差异。评分标准将评分与评分者智能解耦，在评分标准中，官方答案几乎承担了所有工作。
Grading Needs a Rubric, Not Intelligence
Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit rubric. We test this claim as the design principle behind any-to-bench: a frontier model reads source documents once, at ingestion, to extract each question and its rubric; lower-cost models then perform all repeated grading work. We evaluate six cost-efficient model configurations from two model families at three reasoning-effort levels. Each configuration answers 24 open-ended examination questions, and each also grades every answer sheet three times, yielding 3,456 per-question grades. Scores depend overwhelmingly on the answer being graded: answer identity explains 95.6% of score variance, whereas judge identity explains only 0.2%. Raising a writer's reasoning effort moves earned scores by as much as 0.143 of full marks, while raising a judge's reasoning effort moves assigned scores by at most 0.006. Six frontier-tier judges, added as a check, reproduce these scores and are no more reliable as a panel. Two ablations then decompose the rubric on the same questions and answers. Removing its criteria and levels while keeping the official answer changes nothing measurable. Removing the official answer as well collapses reliability (ICC 0.888 to 0.628), inflates scores, and makes judge reasoning effort matter again. The rubric is what decouples grading from judge intelligence, and within the rubric the official answer does nearly all the work. We find no evidence of length preference or same-family preference under rubric-anchored grading.