Minh-Loi Nguyen

Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen et al.

Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is challenging: visually realistic outputs often violate physical laws, temporal consistency, or task logic, while conventional metrics and monolithic Vision-Language Model (VLM) judges fail to generalize or provide precise diagnostic value. We present RoboGaze, a training-free, multi-agent VLM framework that provides structured, interpretable evaluation for generated robot-manipulation videos. Given a task instruction and video, RoboGaze operates via a three-stage pipeline: task-scene grounding, dimension-specific specialist routing, and critic-based verification. It outputs temporally localized glitch reports categorized under a novel 6-dimension, 30-type robotics-specific taxonomy. To benchmark RoboGaze, we introduce a human-validated dataset of 382 clips spanning simulated and real-world multi-view manipulation. Evaluating eight open-source and proprietary VLM backbones, RoboGaze dramatically outperforms zero-shot baselines, improving description-F1 by up to +43 points and temporal alignment (F1 x IoU) by up to +37 points, closing approximately 85% of the gap to the human ceiling. Furthermore, its critic verifier mitigates the "cry-wolf" false-positive flaw of standard VLMs, lifting clean-clip accuracy from under 25% to over 80%. RoboGaze offers a scalable, highly interpretable diagnostic tool for the rigorous evaluation of robot world models.

Minh-Loi Nguyen, Xuan-Vu Le, Long-Bao Nguyen et al.

Traditional image captioning methods often struggle to generate comprehensive, context-rich descriptions, especially for details not directly observable from visual cues. To overcome this, we propose a novel retrieval-augmented image captioning framework that generates captions with deeper insights, such as object attributes, event context, and underlying significance, by leveraging external knowledge. Our approach features a hierarchical multi-modal article retrieval mechanism that moves beyond monolithic text entities. This retrieval considers article structure-aware features, including weighted textual components (e.g., headlines, body sections) and visual placement patterns, alongside multi-faceted similarity computations (content--visual, visual--visual, and discourse positioning). A subsequent contextual relevance refinement stage further enhances the retrieved information. The retrieved articles then serve as the knowledge base for caption generation: first, a VLM generates a concise image description; second, we segment relevant information from the retrieved articles based on this description; and finally, an LLM utilizes both the description and extracted knowledge to generate a comprehensive, contextually detailed caption. We participated in the ACM Multimedia EVENTA 2025 Challenge and achieved 5th place with an overall score of 0.2824 on the private test set of the OpenEvent-V1 dataset. Source code is publicly released at https://github.com/mf0212/EVENTA-Challange.

Xuan-Vu Le, Minh-Loi Nguyen, Khanh-Duy Le et al.

Story writing is a popular yet complex creative activity that requires organization of ideas and iterative exploration, particularly during early-stage ideation. While many AI-based writing assistants have been developed, existing approaches primarily focus on generating long-form coherent text and improving user controllability during text production, providing limited support for brainstorming, connecting ideas, and validating alternative narrative flows. We present GraphStory, an interactive writing support system that leverages a graph-based representation to provide a comprehensive view of narrative structure and facilitate ideation. The system enables users to organize and connect plot points, explore alternative branches, and validate evolving narratives through an integrated story generation workflow. It further provides a structured interface to support efficient iteration over multiple story paths. Results from a user study with professional and semi-professional writers show that GraphStory reduces the effort of organizing narrative structures and better supports creativity and exploration compared to normal AI-based writing workflows.