Case-to-Statute Retrieval—identifying applicable statutes from case facts—is essential for judicial efficiency but remains underexplored in low-resource languages like Vietnamese, where annotated legal corpora are scarce and domain-specific challenges persist. To advance research in this setting, we introduce ViCSR, a new benchmark of 10,000 Vietnamese criminal judgments and 1,122 statutory articles with citation-based relevance labels. We further propose a lightweight two-stage framework that performs efficient candidate retrieval with a fine-tuned Vietnamese bi-encoder and leverages citation structure for reranking with a compact heterogeneous GNN. Experiments show clear improvements over strong sparse and dense baselines, highlighting the importance of both domain adaptation and structure-aware modeling in low-resource legal IR. We will publicly release the benchmark, code, and checkpoints.

Significant advancements have been achieved in both fields of Natural Language Processing (NLP) and Computer Vision (CV) with the advent of Multimodal Large Language Models (MLLMs), sometimes referred to as large vision-language models (LVMs). MLLMs show promising ability in multimodal tasks, such as image captioning, visual question answering, etc. However, there is a concerning trend associated with the advancement in MLLMs. These models exhibit an inclination to generate hallucinations and misleading facts, resulting in seemingly plausible yet factually spurious content. To address these challenges, our team, DeepSIX, leverages recent advances in MLLMs to enhance the ability to detect hallucination and verify factual information within the scope of the ACM MM 2025 grand challenge 8: Truthful and Responsible Multimodal Learning (ResMM). We participated in both tasks: Multimodal Hallucination Detection (Task 1) and Multimodal Fact Checking (Task 2). Our approach leverages the interpretive power of the vision and language components of vision language models (VLMs) to analyze and summarize insights from text and images. It performs contextual reasoning by uncovering semantic relationships among entities in the text and objects in the images. By employing diverse prompting techniques, our method deconstructs critical entities in the text, effectively uncovers implicit relationships between text and images, and identifies hallucinations and false facts. Experimental results demonstrate the strength of our approach: it achieved second place in the Hallucination Detection task and third place in the Fact Verification task, confirming the potential of LLM-based methods in MLLMs. We open-source our code at https://github.com/JAIST-DeepSIX/ACMMM25