Video anomaly understanding (VAU) is critical for real-world scenarios. Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies. However, progress in anomaly localization is still limited by two key issues. First, most existing video anomaly datasets only annotate segments that are clearly inconsistent with the context, often omitting subsequent segments that are semantically part of the same abnormal event. Second, the field lacks systematic evaluation protocols. To bridge these gaps, we introduce VALU, a new benchmark that explicitly defines anomalies across five semantic levels and provides comprehensive temporal boundaries and detailed textual descriptions for each. Based on these annotations, we design three evaluation tasks that comprehensively assess models’ capabilities across different dimensions, including temporal grounding, anomaly localization, and anomaly detail discrimination. Evaluation results reveal persistent challenges in current models’ capabilities on VAU. We further analyze and discuss these findings, and hope that both VALU and insights will advance research in VAU and the development of Video-LLMs. Our benchmark will be publicly available.

Large Vision-Language Models (LVLMs) have a significant issue with object hallucinations, where researchers have noted that LVLMs often mistakenly determine objects as present in images where they do not actually exist. Some recent studies evaluate the occurrence of object hallucinations by asking LVLMs whether they see objects that do not exist in input images. However, we observe that these evaluation methods have some limitations, such as the objects being questioned potentially having little relevance to the image. In this paper, we introduce a more challenging benchmark for evaluating object hallucinations by removing objects from images and then asking the model whether it can still see the removed objects. Our evaluation result reveals that LVLMs suffer from severe hallucinations, as they often still claim to see the removed objects. Through our analysis, we find that biases in training result in LVLMs lacking guidance on learning about the absence of objects, which in turn leads to a lack of ability to determine that objects do not exist in images. To address this issue, we further propose oDPO, a direct preference optimization objective based on visual objects. By guiding LVLMs to learn to determine the existence of objects, oDPO effectively alleviates object hallucinations. It achieves more competitive results than other hallucination mitigation approaches across multiple object hallucination benchmarks and enhances the performance of LVLMs in various vision-language tasks.

Existing research in multi-hop questions has identified two reasoning modes: latent reasoning and factual shortcuts, but has not deeply investigated how these modes differ during inference. This impacts both model generalization ability and downstream reasoning tasks. In this work, we systematically examine these distinctions and propose a simple and efficient classification metric, Attribute Rate Ratio (ARR). First, we construct specialized datasets corresponding to the two reasoning modes based on our proposed criteria. Then, using reverse engineering methods, including attention knockout and logit lens techniques, we reveal that subject representations differ significantly across modes: latent reasoning encodes bridge-related information for final answer extraction, while factual shortcuts bypass intermediate reasoning and resemble single-hop factual queries. Finally, our proposed ARR achieves around 90% accuracy on our datasets and demonstrates effectiveness in RAG conflict scenarios, showing that model behavior under conflicting prompts is closely tied to its underlying reasoning mode. Our findings and proposed metric have significant potential for advancing LLM development and applications.