collaborators

12 papers

cs.CL2026

Beyond Single-Negative Preference: Multi-Negative DPO for LLM-Centric Historical Entity Linking

Tien Nam Nguyen, Emanuela Boros, Ahmed Hamdi +3

Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated with only one negative candida…

cs.SE2026

On Behavioral Alignment of Model-Code and Human-Code Understandability via Behavioral Proxies

Xiaokai Rong, Aashish Yadavally, Anh H. N. Nguyen +2

Code understandability is a critical aspect of software quality. Prior research has largely focused on this attribute from a human-centric or code-centric perspective, while it sho…

cs.SE2026

W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases

Hridya Dhulipala, Rajesh Ombase, Michael Wang +1

Retrieval-Augmented Generation (RAG) enables large language models to incorporate external knowledge during generation, improving factual grounding and domain adaptability. However…

cs.SE2026

Post-Hoc Attention Steering of Large Language Models for Robust Code Understanding under Obfuscation

Xiaokai Rong, Aashish Yadavally, Tien N. Nguyen

Code obfuscation is widely used in software systems and malware to conceal program logic and hinder analysis, posing significant challenges for both human developers and automated…

cs.SE2026

AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation

Xiaokai Rong, Hridya Dhulipala, Aashish Yadavally +1

Developers often need to adapt into their projects the code generated from LLMs or code snippets from online forums. However, integrating them into an existing repository remains c…

cs.SE2026

Can Perplexity Serve as a Cognitive Signal for Code Understandability?

Xiaokai Rong, Mohammadali Sefidi Esfahani, Aashish Yadavally +2

Recent work suggests that token-level perplexity from large language models can align with localized human confusion during code comprehension. This raises a natural question: can…