The paper introduces VERDICT, a method for validating optical chemical structure recognition (OCSR) outputs by leveraging agreement among multiple recognizers rather than pixel‑space re‑rendering. On 263 ACS journal images, agreement achieved an AUROC of 0.916, far surpassing the 0.547 AUROC of re‑rendering similarity. VERDICT was applied to PMC Open Access, yielding over 6,000 high‑precision structure labels, and is integrated into SES AI’s Molecular Universe platform for image‑based molecular search.
By Yani Guan, Dengpan Dong, Shuang Luo, Zi Wei, Joah Han, Dan Hannah, Yumin Zhang, Qichao Hu, Kang Xu
arXiv:2609.26168v1 Announce Type: cross
Abstract: Recent work reports that vision--language models (VLMs) struggle to establish and maintain stable reference in repeated reference games. Rather than...
By Joseph Bingham
arXiv:2608.08477v5 Announce Type: replace
Abstract: We build VectraYX-Vision-1B, a sub-2B Spanish/LATAM cybersecurity vision-language model for offline use, and measure what limits its visual groundi...
By Juan S. Santillana
arXiv:2609.24565v2 Announce Type: replace
Abstract: A connectome-constrained model of the fly visual system, optimized for motion and then frozen, can be driven over architectural drawings by prescri...
By Dmitry Kuklev
arXiv:2606. 07882v1 Announce Type: cross Abstract: Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different internal representations.
By Yousef Radwan
The paper evaluates the claim that vision‑language models (VLMs) outperform task‑specific vision backbones for UAV power‑line defect assessment using the ElecVQA‑Bench benchmark. Across various evaluation settings—partitioning, item sets, label spaces, replication, resolution, and side information—the performance gap between VLMs and traditional backbones is minimal or even reversed when controlling for resolution and token budget. The study concludes that VLM superiority is not universally supported and emphasizes the importance of rigorous benchmark audits.
By Linghao Zhang, Siyu Xiang, Junwei Kuang, Peiyu Yi