MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering

Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-based Retrieval Ranking (MKG-Rank), a knowledge graph-enhanced framework that enables English-centric LLMs to perform multilingual medical QA. Through a word-level translation mechanism, our framework efficiently integrates comprehensive English-centric medical knowledge graphs into LLM reasoning at a low cost, mitigating cross-lingual semantic distortion and achieving precise medical QA across language barriers. To enhance efficiency, we introduce caching and multi-angle ranking strategies to optimize the retrieval process, significantly reducing response times and prioritizing relevant medical knowledge. Extensive evaluations on multilingual medical QA benchmarks across Chinese, Japanese, Korean, and Swahili demonstrate that MKG-Rank consistently outperforms zero-shot LLMs, achieving maximum 35.03% increase in accuracy, while maintaining an average retrieval time of only 0.0009 seconds.

ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA

Recent advances in large language models (LLMs) have significantly improved multi-hop question answering (QA) through direct Chain-of-Thought (CoT) reasoning. However, the irreversible nature of CoT leads to error accumulation, making it challenging to correct mistakes in multi-hop reasoning. This paper introduces ReAgent: a Reversible multi-Agent collaborative framework augmented with explicit backtracking mechanisms, enabling reversible multi-hop reasoning. By incorporating text-based retrieval, information aggregation and validation, our system can detect and correct errors mid-reasoning, leading to more robust and interpretable QA outcomes. The framework and experiments serve as a foundation for future work on error-tolerant QA systems. Empirical evaluations across three benchmarks indicate ReAgent's efficacy, yielding average about 6\% improvements against baseline models.

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such as medicine. Existing fact-checking with grounding documents methods face two main challenges: (1) they struggle to understand complex multihop relations in long documents, often overlooking subtle factual errors; (2) most specialized methods rely on pairwise comparisons, requiring multiple model calls, leading to high resource and computational costs. To address these challenges, we propose \textbf{\textit{GraphCheck}}, a fact-checking framework that uses extracted knowledge graphs to enhance text representation. Graph Neural Networks further process these graphs as a soft prompt, enabling LLMs to incorporate structured knowledge more effectively. Enhanced with graph-based reasoning, GraphCheck captures multihop reasoning chains which are often overlooked by existing methods, enabling precise and efficient fact-checking in a single inference call. Experimental results on seven benchmarks spanning both general and medical domains demonstrate a 6.1\% overall improvement over baseline models. Notably, GraphCheck outperforms existing specialized fact-checkers and achieves comparable performance with state-of-the-art LLMs, such as DeepSeek-V3 and OpenAI-o1, with significantly fewer parameters.

Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection

Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-scale, real-world images. Our findings indicate that: 1) Pre-trained models can cluster the features of real images effectively. 2) Models with pre-trained weights can approximate an optimal generalization solution at a specific training step, but it is extremely unstable. Based on these facts, we propose a simple yet effective training method called Learning on Less (LoL). LoL utilizes a random masking mechanism to constrain the model's learning of the unique patterns specific to a certain type of diffusion model, allowing it to focus on less image content. This leverages the inherent strengths of pre-trained weights while enabling a more stable approach to optimal generalization, which results in the extraction of a universal feature that differentiates various diffusion-generated images from real images. Extensive experiments on the GenImage benchmark demonstrate the remarkable generalization capability of our proposed LoL. With just 1% training data, LoL significantly outperforms the current state-of-the-art, achieving a 13.6% improvement in average ACC across images generated by eight different models.

Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation

Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in robustness against post-processing. To tackle these, we propose a guided and multi-scale feature aggregated network for IFL. Spectifically, in order to comprehensively learn the noise feature under different types of forgery, we develop an effective noise extraction module in a guided way. Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales. Moreover, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features using different receptive fields to improve the accuracy and robustness of forgery localization. Expensive experiments on 5 public datasets have shown that our proposed model outperforms several the state-of-the-art methods, specially on small region forged image.

Guided and Fused: Efficient Frozen CLIP-ViT with Feature Guidance and Multi-Stage Feature Fusion for Generalizable Deepfake Detection

The rise of generative models has sparked concerns about image authenticity online, highlighting the urgent need for an effective and general detector. Recent methods leveraging the frozen pre-trained CLIP-ViT model have made great progress in deepfake detection. However, these models often rely on visual-general features directly extracted by the frozen network, which contain excessive information irrelevant to the task, resulting in limited detection performance. To address this limitation, in this paper, we propose an efficient Guided and Fused Frozen CLIP-ViT (GFF), which integrates two simple yet effective modules. The Deepfake-Specific Feature Guidance Module (DFGM) guides the frozen pre-trained model in extracting features specifically for deepfake detection, reducing irrelevant information while preserving its generalization capabilities. The Multi-Stage Fusion Module (FuseFormer) captures low-level and high-level information by fusing features extracted from each stage of the ViT. This dual-module approach significantly improves deepfake detection by fully leveraging CLIP-ViT's inherent advantages. Extensive experiments demonstrate the effectiveness and generalization ability of GFF, which achieves state-of-the-art performance with optimal results in only 5 training epochs. Even when trained on only 4 classes of ProGAN, GFF achieves nearly 99% accuracy on unseen GANs and maintains an impressive 97% accuracy on unseen diffusion models.