Large vision-language models (LVLMs) have made rapid advancements; however, existing benchmarks for evaluating their instruction-following abilities focus on purely textual constraints, e.g., format constraints and role-playing, which assess only language-level adherence and are not related to vision. To bridge this gap, we introduce VC-IF, a benchmark that involves visual constraints connecting instructions to explicit operations over image content, thereby directly probing visual spatial understanding and compositional compliance. VC-IF defines atomic constraints, systematically combining mixed settings, scaling from single- to multi-constraint scenarios, and single- and multi-turn scenarios. We evaluate 16 popular LVLMs and find that models performing well under text-only constraints may struggle when faced with visual constraints, highlighting significant gaps in their capabilities. Further, we conduct scaling analyses across varying numbers of constraints and dialogue turns, revealing performance trends as constraint complexity increases along different dimensions. Finally, we synthesize a large training dataset and propose VC-PO, a reinforcement learning strategy that uses a hybrid verifier mechanism to compute reward signals for multiple constraints. Applying VC-PO to Qwen3-VL-8B-Instruct improves its overall VC-IF score from 43.74 to 55.18 (+11.44). The VC-PO-trained model remains stable as the number of turns and constraints increases. Our data and code are available at VC-IF.

The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models’ knowledge capacity and reliability. In this paper, we introduce the first factuality-based visual question-answering benchmark in Chinese, named ChineseSimpleVQA, aimed at assessing the visual factuality of LVLMs across 8 major topics and 56 subtopics. The key features of this benchmark include a focus on the Chinese language, diverse knowledge types, a multi-hop question construction, high-quality data, static consistency, and easy-to-evaluate through short answers. Moreover, we contribute a rigorous data construction pipeline and decouple the visual factuality into two parts: seeing the world (i.e., object recognition) and discovering knowledge. This decoupling allows us to analyze the capability boundaries and execution mechanisms of LVLMs. Subsequently, we evaluate 34 advanced open-source and closed-source models, revealing critical performance gaps within this field.

To present, multilingual language models trained using various pre-training tasks like mask language modeling (MLM) have yielded encouraging results on a wide range of downstream tasks. Despite the promising performances, structural knowledge in cross-lingual corpus is less explored in current works, leading to the semantic misalignment. In this paper, we propose a new pre-training task named Structural Contrast Pretraining (SCP) to align the structural words in a parallel sentence, enhancing the models’ ability to comprehend cross-lingual representations. Concretely, each structural word in source and target languages is regarded as a positive pair in SCP. Since contrastive learning compares positive and negative pairs, an increase in the frequency of negative pairings could enhance the performance of the resulting model. Therefore, we further propose Cross-lingual Momentum Contrast (CL-MoCo) to increase the number of negative pairs by maintaining a large size of the queue. CL-MoCo extends the original Moco approach into cross-lingual training and jointly optimizes the source-to-target language and target-to-source language representations, resulting in a more suitable encoder for cross-lingual transfer. We conduct extensive experiments to validate the proposed approach on three cross-lingual tasks across five datasets such as MLQA, WikiAnn, etc, and results prove the effectiveness of our method.