Ilan Theodoro, Jordão Bragantini, Loïc A. Royer · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31566 · open-access PDF
대형 언어 모델은 코딩, 수학 및 쓰기와 같은 텍스트 첫 번째 작업에서 뛰어난 성능을 보여주었습니다. 최근의 노력은 모델의 훈련을 변화시키고 있으며 종종 단백질 구조와 생물학적 이미지를 비롯한 텍스트를 넘어 고도로 전문화된 도메인에 존재하는 과학적 문제를 포함합니다. 그러나 이러한 시나리오에서 전문화된 알고리즘에 비해 일반적인 목적의 정확성은 여전히 의문을 제기하고 있습니다. 이러한 도메인 중 하나는 세포 추적입니다.
Machine translation (Meta M2M100); the English original follows.
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
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.31566, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange