6 papers · 1 filter
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This pape…
MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
Boyuan Chen, Minghao Shao, Abdul Basit +2
As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and s…
CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman +1
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action pr…
PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on re…
BRAVE: Brain-Controlled Prosthetic Arm with Voice Integration and Embodied Learning for Enhanced Mobility
Abdul Basit, Maha Nawaz, Muhammad Shafique
Non-invasive brain-computer interfaces (BCIs) have the potential to enable intuitive control of prosthetic limbs for individuals with upper limb amputations. However, existing EEG-…
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni +1
Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a v…