VisionQ is a new benchmark for qualitative analysis in computer vision that evaluates vision‑language models (VLMs) on criterion‑conditioned visual discrimination. It is built from over 1,800 peer‑reviewed comparison figures in CVPR and ICCV papers, linking each image crop to author‑stated visual claims through 3,911 hand‑annotated data points. The benchmark includes a 51‑leaf taxonomy of visual criteria, a protocol that hides method identities and reports accuracy per criterion, and a DPO‑tuned Gemma‑4‑E4B judge that improves accuracy on a held‑out test set.
By Vu Dinh Xuan, Duc-Hai Nguyen, Minh-Dung Dao, Vu Quynh Giao, Quang Hong Nguyen, Binh-Son Hua, Barry O'Sullivan, David Murphy, Hoang D. Nguyen
arXiv:2607. 27066v1 Announce Type: cross Abstract: Scientific figure assessment in peer review differs fundamentally from general image quality evaluation: a figure must be visually legible, faithfully support the manuscript's claims, and communicate evidence with a clear visual hierarchy.
By Chuanzhi Xu, Zihan Deng, Huiqi Liang, Chengkun Yue, Zhanlin Cui, Pengfei Ye, Weidong Cai
arXiv:2609.00654v1 Announce Type: new
Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
By Qiming Bao, Ne\c{s}et \"Ozkan Tan, Siyuan Wang, Mark Gahegan
arXiv:2606. 00065v1 Announce Type: cross Abstract: Automated extraction of materials composition-property data from scientific literature has advanced considerably with the development of large language model-based pipelines; however, existing frameworks remain limited to textual and tabular content, overlooking the substantial proportion of quantitative property data reported exclusively in scientific figures.
By Aritra Roy, Enrico Grisan, Chiara Gattinoni, John Buckeridge
OpenDiscoveryTrace is a public dataset of 558 complete AI scientific agent trajectories that records the reasoning process—thoughts, tool calls, observations, errors, revision triggers, and confidence—across 124 scientific tasks in drug discovery, materials science, genomics, and literature analysis. The dataset includes seven models (three frontier models and four open‑weight models) and 60 live‑retrieval variants, providing a balanced view of performance and error patterns. Pilot analysis shows that process traces reveal behavioral differences invisible to output‑only evaluation, such as differing error rates and types among frontier models.
By Aayam Bansal, Keertan Balaji
The paper investigates whether panels of vision‑language models (VLMs) can reliably judge image aesthetics. It shows that a panel of holistic judges rarely outperforms its best member, but when each model scores images on five rubric‑defined dimensions and these dimension scores are fused across model families, the panel consistently beats the best single VLM on two datasets (EVA and PARA). The study demonstrates that the value of a panel depends on the type of input it receives, and that dimension‑based fusion yields measurable gains at the cost of additional labeling and API usage.
By Amit Jadhav, Shaurya Beriwala, Beomjin Kim