collaborators

10 papers

cs.CL2026

VietMed-MCQ: A Consistency-Filtered Data Synthesis Framework for Vietnamese Traditional Medicine Evaluation

Huynh Trung Kiet, Dao Sy Duy Minh, Nguyen Dinh Ha Duong +3

Large Language Models (LLMs) have demonstrated remarkable proficiency in general medical domains. However, their performance significantly degrades in specialized, culturally speci…

cs.LG2026

Unlocking Compositional Generalization in Continual Few-Shot Learning

Phu-Quy Nguyen-Lam, Phu-Hoa Pham, Dao Sy Duy Minh +3

Object-centric representations promise a key property for few-shot learning: Rather than treating a scene as a single unit, a model can decompose it into individual object-level pa…

cs.CV2026

Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery

Chi-Nguyen Tran, Dao Sy Duy Minh, Huynh Trung Kiet +3

Cross-view geo-localization (CVGL), which matches an oblique drone view to a geo-referenced satellite tile, has emerged as a key alternative for autonomous drone navigation when GN…

cs.LG2026

MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound

Phu-Hoa Pham, Chi-Nguyen Tran, Nguyen Lam Phu Quy +3

Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decision boundaries. Despite thes…

cs.CL2026

Training-Free Cultural Alignment of Large Language Models via Persona Disagreement

Huynh Trung Kiet, Dao Sy Duy Minh, Tuan Nguyen +5

Large language models increasingly mediate decisions that turn on moral judgement, yet a growing body of evidence shows that their implicit preferences are not culturally neutral.…

cs.SE2026

MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution

Dao Sy Duy Minh, Tran Chi Nguyen, Trung Kiet Huynh +3

We present MEMRES, an agentic system for Python dependency resolution that introduces a multi-level confidence cascade where the LLM serves as the last resort. Our system combines:…