3 papers
cs.CV2026
Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench
Fenfen Lin, Yesheng Liu, Haiyu Xu +7
Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models…
cs.CL2025
FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions
Bowen Qin, Chen Yue, Fang Yin +26
We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evalua…
cs.CL2025
Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT
Yesheng Liu, Hao Li, Haiyu Xu +9
Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output…