We introduce M4-RAG, a massive-scale benchmark covering 42 languages and 56 dialects with over 80,000 culturally diverse image-question pairs to evaluate retrieval-augmented VQA. Our systematic evaluation reveals that while RAG consistently benefits smaller Vision-Language Models (VLMs), it fails to scale to larger models and often degrades their performance, exposing a critical mismatch between model size and current retrieval effectiveness.

We propose HybridNet, a data-efficient framework that leverages hybrid active learning to select the most informative samples, drastically reducing labeling cost. We also propose a lightweight Reasoning-Aware Classifier (RAC) for challenging cases, which combines Vision–Language Model (VLM) features with reasoning from a Multimodal Large Language Model (MLLM) to further improve detection performance and provide human- interpretable explanations.

In this work, we address a critical gap in Vision-Language Model (VLM) evaluation by introducing MTabVQA, a novel benchmark for multi-tabular visual question answering. Our benchmark comprises 3,745 complex question-answer pairs that require multi-hop reasoning across several visually rendered table images, simulating real-world documents. We benchmark state-of-the-art VLMs, revealing significant limitations in their ability to reason over complex visual data. To address this, we release MTabVQA-Instruct, a large-scale instruction-tuning dataset. Our experiments demonstrate that fine-tuning with our dataset substantially improves VLM performance, bridging the gap between existing benchmarks that rely on single or non-visual tables.

In this work, we address the growing security challenges in IoT networks by conducting a comprehensive comparative analysis of machine learning classifiers for intrusion detection. We evaluated five distinct models on two real-world IoT network traffic datasets to identify the most effective algorithms for detecting malicious activity. Our findings show that tree-based models, specifically Random Forest and Decision Trees, deliver outstanding performance, achieving accuracies exceeding 99%. This research provides a clear benchmark and practical guidance for developing robust and high-performance security systems to protect vulnerable IoT environments.