Syed Rameez Naqvi

Md Mostafizer Rahman, Ariful Islam Shiplu, Yutaka Watanobe et al.

Contextual embeddings generated by LLMs exhibit strong positional inductive biases, which can limit their ability to fully capture long-range, order-sensitive dependencies in highly structured source code. Consequently, how to further refine and enhance LLM embeddings for improved code understanding remains an open research question. To address this gap, we propose a hybrid LLM-RNN framework that reinforces LLM-generated contextual embeddings with a sequential RNN architecture. The embeddings reprocessing step aims to reinforce sequential semantics and strengthen order-aware dependencies inherent in source code. We evaluate the proposed hybrid models on both benchmark and real-world coding datasets. The experimental results show that the RoBERTa-BiGRU and CodeBERT-GRU models achieved accuracies of 66.40% and 66.03%, respectively, on the defect detection benchmark dataset, representing improvements of approximately 5.35% and 3.95% over the standalone RoBERTa and CodeBERT models. Furthermore, the CodeT5-GRU and CodeT5+-BiGRU models achieved accuracies of 67.90% and 67.79%, respectively, surpassing their base models and outperforming RoBERTa-BiGRU and CodeBERT-GRU by a notable margin. In addition, CodeT5-GRU model attains weighted and macro F1-scores of 67.18% and 67.00%, respectively, on the same dataset. Extensive experiments across three real-world datasets further demonstrate consistent and statistically significant improvements over standalone LLMs. Overall, our findings indicate that reprocessing contextual embeddings with RNN architectures enhances code understanding performance in LLM-based models.

Syed Rameez Naqvi, Lu Peng

Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these objectives. We present MOLPAQ, a modular quantum-classical generator that assembles molecules from quantum-generated latent patches. A \b{eta}-VAE pretrained on QM9 learns a chemically aligned latent manifold; a reduced conditioner maps molecular descriptors into this space; and a parameter-efficient quantum patch generator produces entangled node embeddings that a valence-aware aggregator reconstructs into valid molecular graphs. Adversarial fine-tuning with a latent critic and chemistry-shaped reward yields 100\% RDKit validity, 99.75\% novelty, and 0.905 diversity. Beyond aggregate metrics, the pretrained quantum generator, steered by the conditioner, improves mean QED by approx. 2.3\% and increases aromatic motif incidence by approx. 10-12\% relative to a parameter-matched classical generator, highlighting its role as a compact topology-shaping operator.