8 papers
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents
Do Xuan Trong, Duc Anh Nguyen, Do Xuan Long +12
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesi…
Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training
Cuong Dang, Hoang Anh Just, Ruoxi Jia
Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study the \emph{supervision horizon…
Retrieved But Not Reliable: A Survey on Attacks, and Defenses in Retrieval-Augmented Generation
Minh Tran, Cuong Dang, Tuc Nguyen +10
Retrieval-Augmented Generation (RAG) enhances large language models by grounding outputs in external knowledge, improving factuality and reducing hallucinations. At the same time,…
Capacity-Dependent Effects of Data Selection for Reasoning
Cuong Dang, Hoang Anh Just, Ruoxi Jia
In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's current distribution. Recent likelih…
How Robust Are LLMs to Vietnamese Dialects?
Minh Tran, Trinh Chau, Thanh-Nhan Le +4
Large Language Models (LLMs) are typically evaluated on standard written Vietnamese, yet everyday communication frequently involves regional dialects that preserve meaning but diff…
The Confidence Trap: Calibration Attacks for Graph Neural Networks
Cuong Dang, Jiahao Zhang, Hieu Ta Quang +3
While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations…
URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
Vinh Nguyen, Cuong Dang, Jiahao Zhang +6
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluati…
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models
Cuong Dang, Dung D. Le, Thai Le
Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Tra…