Newest papers

CellTrack-Bench: Microscopic Cell Tracking Benchmark for Vision-Language Models

Ilan F. S. Theodoro, Jordão Bragantini, Löıc A. Royer · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31566 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

大型语言模型在编码,数学和写作等文本首要任务上表现出色。最近的努力正在改变模型的培训,包括通常生活在文本以外的高专业领域的科学问题,如蛋白质结构和生物图像。然而,在这些场景中,它们的一般用途的准确性与专门算法相比仍然存在质疑。 其中一个领域是细胞跟踪,这需要对细胞动态的空间时间意识。 为了评估这一点,我们创建了一个新的基准,CellTrack-Bench,该基准评估了Vision-Language Models(VLMs)对专业追踪器的细胞跟踪性能。 完整基准从四种成像模式的15个标准细胞跟踪数据集中获取,并包含了1537个问题,与9

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

Abstract

Large language models have shown outstanding performance on text-first tasks such as coding, math, and writing. Recent efforts are shifting models' training to include scientific problems that often live in highly specialized domains beyond text, such as protein structure and biological images. However, in these scenarios, their general-purpose accuracy compared with specialized algorithms remains in question. One such domain is cell tracking, which requires spatiotemporal awareness of cell dynamics. To evaluate this, we created a new benchmark, CellTrack-Bench, which assesses Vision-Language Models' (VLMs) cell-tracking-related performance against specialized trackers. The full benchmark draws from 15 standard cell-tracking datasets across four imaging modalities and comprises 1,537 problems corresponding to 9,041 cell-linking decisions. We selected a hard subset requiring greater reasoning effort by identifying failures shared across distinct algorithmic trackers. With this proposed dataset, we: (i) evaluated different environment harnesses for VLMs; (ii) benchmarked 9 frontier closed-source models and 13 open-weight models; and (iii) provided a detailed analysis of their failure modes. Code, the stored model outputs, and a pipeline that regenerates the benchmark from the source datasets are available at https://github.com/royerlab/celltrack-bench.

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Topics: Benchmark (surveying), Pipeline (software), Computer science, Domain (mathematical analysis), Artificial intelligence, Machine learning

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