Newest papers

ProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry

Matt Turk, Brandon L. Neel, Diana M. Lopez, Zachary Daniel Smith, Grace N. Johnecheck, James Brown Golden III · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26829 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们询问基准得分多少反映了差距,以及基准得分多少反映了模型周围的评估任务配置,所以我们研究ProteinGeoBench,一套131个蛋白质结构任务,使用四个边缘模型的4249次调试,以及26个模型中的4486次进一步试验。四个边缘模型中的三个在数值上略有好一些。实际上,数值任务在0.031(0785至0.816)之间没有任何差异,而第四个数据得分为0.707,所有三个数据差距都排除在零位。四个模型中的三个在元数据搜索上做得更好,而不是在数值计算上。第四个模型在数值上略有好一些。 数值任务是28个(0785至0.816)

Machine translation (Meta M2M100); the English original follows.

Abstract

Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably. We ask how much of a benchmark score reflects that gap and how much reflects the evaluation configuration around the model. We study ProteinGeoBench, a suite of 131 protein-structure tasks, using 4,249 rollouts from four frontier models and 4,486 further trials across 26 models. Three of the four frontier models fall within 0.031 of one another (0.785 to 0.816) with no difference between them resolved, while the fourth scores 0.707 and all three gaps to it exclude zero. Three of the four do better on metadata lookup than on numeric calculation. The fourth is slightly better on numeric. Numeric tasks are 28 of the 131 but half of the twelve tasks that all four models fail. The ranking also depends on the evaluation budget: with one run per task, 23.9% of bootstrap replicates put a different model first. On a selected 15-task slice, removing network access lowers scores more than blocking eight structural-database domains, but the two contrasts were graded in different passes and cannot be cleanly decomposed. Swapping in each model's own command-line agent changes scores by -0.071 to +0.086, a comparison too small to rule out moderate harness effects. Across 26 models, numeric performance rises with non-numeric performance rather than staying flat. The experiments use different task subsets, so they cannot rank model, harness, access and task composition against one another. Evaluations should report results by task family, along with the number of runs, the harness and the access conditions.

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Topics: Task (project management), Benchmark (surveying), Computer science, Metadata, Ranking (information retrieval), Frontier

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange