The paper introduces AstroVLBench, a benchmark of 4,100 expert‑verified instances covering optical imaging, radio interferometry, multi‑wavelength photometry, time‑domain light curves, and optical spectroscopy. Six leading vision‑language models were evaluated, revealing that performance varies strongly with modality and is not predicted by general benchmark rankings. Mechanistic ablations identified three bottlenecks: prompt grounding depends on model and task, presenting one‑dimensional data as tables rather than plots improves light‑curve classification, and a significant fraction of correct predictions are supported by physically inconsistent reasoning.
By Wenke Ren, Hengxiao Guo, Wenwen Zuo, Xiaoman Zhang
arXiv:2606. 11190v1 Announce Type: new Abstract: Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each succeeds, when each fails, and when cross-modal training helps at all -- a gap that leaves practitioners, especially in scientific domains like biomedicine or astrophysics, with heterogeneous instruments and multiple levels of organization and measurement, unable to diagnose why standard methods underperform the best single modality.
By Ilay Kamai, Hugues Van Assel, Aviv Regev, Hagai B. Perets, Randall Balestriero
arXiv:2609.06245v1 Announce Type: cross
Abstract: Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often str...
By Yixin Wan, Tianle Zheng, Kai-Wei Chang
arXiv:2607. 00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy.
By Ved G. Shah, Nabeel Rehemtulla, Adam A. Miller, Sushant Sharma Chaudhary, Michael W. Coughlin, Antoine Le Calloch, Matthew J. Graham, Joahan Castaneda Jaimes, Theophile Jegou du Laz, Ashish A. Mahabal, Frank J. Masci, Josiah Purdum, Reed Riddle, Jesper Sollerman, Anastasia Wei, Mansi M. Kasliwal
arXiv:2610.14173v1 Announce Type: cross
Abstract: Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable...
By Daniel J. Evans, J. P. Naiman, JooYoung Seo, J. Stephen Downie
Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each succeeds, when each fails, and when cross-modal training helps at all -- a gap that leaves practitioners, especially in scientific domains like biomedicine or astrophysics, with heterogeneous instruments and multiple levels of organization and measurement, unable to diagnose why standard methods underperform the best single modality. We develop a unified linear framework that addresses both questions.
arXiv:2510.09302v2 Announce Type: replace-cross
Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, eve...
By Yuying Li, Siyi Qian, Hao Liang, Leqi Zheng, Ruichuan An, Linzhuang Sun, Jiajun Zhang, Wentao Zhang
arXiv:2608.28707v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
By Anoop Senthil
arXiv:2610.02959v1 Announce Type: new
Abstract: Recent text-to-image models have made substantial progress in photorealism, aesthetics, and text-image alignment. Yet visually appealing images can sti...
By Shuai Fu, Jing Gu, Jian Zhou, Zicheng Duan, Gengze Zhou, Qi Wu
arXiv:2605.26380v2 Announce Type: replace-cross
Abstract: Frontier multimodal large language models (MLLMs) have been reported to achieve over 90\% accuracy on fine-grained perception benchmarks. How...
By Jingru Chen, Yiming Liu, Mingtao Chen, Sijie Chen, Richeng Xuan, Liang Yang, Zhichao Hu, Fanyang Lu
arXiv:2601.19060v2 Announce Type: replace-cross
Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on...
By Jeonghwan Kim, Renjie Tao, Sanat Sharma, Jiaqi Wang, Kai Sun, Zhaojiang Lin, Seungwhan Moon, Lambert Mathias, Anuj Kumar, Heng Ji, Xin Luna Dong
arXiv:2611. 06484v1 Announce Type: cross Abstract: Reported performance in small astronomical image datasets can reflect object reuse, field-of-view differences, acquisition settings, and catalogue-derived properties as well as astronomical morphology.
By Eraldo Pereira Marinho