Despite the impressive performance of vision-language models (VLMs) on downstream tasks, their ability to understand and reason about causal relationships in visual inputs remains unclear. Robust causal reasoning is fundamental to solving complex high-level reasoning tasks, yet existing benchmarks often include a mixture of reasoning questions, and VLMs can frequently exploit object recognition and activity identification as shortcuts to arrive at the correct answers, making it challenging to truly assess their causal reasoning abilities. To bridge this gap, we introduce VQA-Causal and VCR-Causal, two new benchmarks specifically designed to isolate and rigorously evaluate VLMs’ causal reasoning abilities. Our findings reveal that while VLMs excel in object and activity recognition, they perform poorly on causal reasoning tasks, often only marginally surpassing random guessing. Further analysis suggests that this limitation stems from a severe lack of causal expressions in widely used training datasets, where causal relationships are rarely explicitly conveyed. We additionally explore fine-tuning strategies with hard negative cases, showing that targeted fine-tuning can improve model’s causal reasoning while maintaining generalization and downstream performance. Our study highlights a key gap in current VLMs and lays the groundwork for future work on causal understanding. We will release the code upon acceptance.

The escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents—a capability fundamentally absent in conventional function-level search paradigms. While recent research has improved alignment between queries and code snippets, retrieving contextually relevant code for certain change request remains underexplored. To bridge this gap, we present RepoAlignBench, the first benchmark designed to evaluate repository-level code retrieval for change request-driven scenarios, encompassing 52k columns. The benchmark shifts the paradigm from function-centric retrieval to holistic repository analysis. In addition, we propose ReflectCode, an adversarial reflection-augmented dual-tower architecture featuring disentangled code_encoder and doc_encoder towers. Our framework dynamically integrates syntactic patterns, function dependency, and semantic expansion intent through LLM. Comprehensive evaluations demonstrate that ReflectCode achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art baselines.