Muhammad Anas Raza

Niful Islam, Muhammad Anas Raza, Mohammad Wardat

Large Language Models (LLMs) have transformed software development and AI applications. While LLMs are designed for text processing, LLM agents extend this capability by enabling autonomous actions, tool use, and multi-step task completion. As this field grows, developers face new challenges in debugging these complex systems. To address this challenge, we present the first empirical study on bug fix patterns in LLM agents. We study buggy posts and code snippets from three platforms: Stack Overflow, GitHub, and HuggingFace Forums. We examine their fix patterns, the components where fixes are applied, and the programming languages and frameworks involved. Furthermore, we introduce AgentDefect, the first benchmark dataset for bugs in LLM agents. The dataset contains 37 runtime buggy instances along with fixed code and test files. Finally, we present SelfHeal, a multi-agent system designed to fix bugs in LLM agents. The system leverages two independent ReAct agents: the fix agent and the critic agent. These agents use tools that provide both internal knowledge (fix rules) and external knowledge (web search) to propose and validate fixes. Our evaluation shows that SelfHeal with Gemini 3 Pro as the backbone LLM outperforms both baseline and state-of-the-art approaches by a significant margin.

Muhammad Umar Farooq, Ali Javed, Khalid Mahmood Malik et al.

The recent realistic creation and dissemination of so-called deepfakes poses a serious threat to social life, civil rest, and law. Celebrity defaming, election manipulation, and deepfakes as evidence in court of law are few potential consequences of deepfakes. The availability of open source trained models based on modern frameworks such as PyTorch or TensorFlow, video manipulations Apps such as FaceApp and REFACE, and economical computing infrastructure has easen the creation of deepfakes. Most of the existing detectors focus on detecting either face-swap, lip-sync, or puppet master deepfakes, but a unified framework to detect all three types of deepfakes is hardly explored. This paper presents a unified framework that exploits the power of proposed feature fusion of hybrid facial landmarks and our novel heart rate features for detection of all types of deepfakes. We propose novel heart rate features and fused them with the facial landmark features to better extract the facial artifacts of fake videos and natural variations available in the original videos. We used these features to train a light-weight XGBoost to classify between the deepfake and bonafide videos. We evaluated the performance of our framework on the world leaders dataset (WLDR) that contains all types of deepfakes. Experimental results illustrate that the proposed framework offers superior detection performance over the comparative deepfakes detection methods. Performance comparison of our framework against the LSTM-FCN, a candidate of deep learning model, shows that proposed model achieves similar results, however, it is more interpretable.