2 citations · 5 across the 32 of their papers we have counts for
36 papers
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit
Hoang Pham, Duc Anh Vu, Duy Tung Doan +2
In search engines and assistants, long-form RAG answers can mislead readers with claims their sources do not support. Faithfulness rewards, the share of supported sentences, train…
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments
Hoang Pham, Cong-Duy Nguyen, Dong Le +2
Persistent textual memory lets language-model agents reuse lessons from past tasks to improve on future multi-step tasks without retraining. Systems such as ACE and Dynamic Cheatsh…
Reinforcing Step-level Reasoning for Effective Self-Correction in LLMs
Vu Duc Anh, Nhat M. Hoang, Do Xuan Long +3
Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs). In this work, we propose…
STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models
Thong Nguyen, Tri Cao, Khoi Le +4
While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure i…
DynaPix: Can Vision-Language Models Identify the Exact Future?
Thong Nguyen, Vinh-Hien Do, Quynh Vo +2
Acting in a physical scene requires knowing its real later state, not a plausible one. Current evaluations often accept words or a realistic-looking image, so the predicted state i…
Predict, Then Retrieve: Cross-Instance Future-State Retrieval from Video Prefixes
Quynh Vo, Thong Nguyen, Vinh-Hien Do +2
We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instance…
When Depth Is Better Told Than Shown: Depth-Ordinal Prompting for Vision-Language Spatial Reasoning
Quynh Vo, Phuc Dao, Cong-Duy Nguyen +1
Vision-language models (VLMs) are expected to reason about physical space -- which object is closer, what lies behind what, and how objects are arranged in 3D -- yet they still str…
TIGER: Text-Conditioned Visual Gated Routing with Acceptance Alignment for Multimodal Speculative Decoding
Quynh Vo, Cong-Duy Nguyen, Ponhvoan Srey +2
Speculative decoding accelerates autoregressive generation by letting a lightweight drafter propose multiple tokens that are verified by a larger target model. Although effective f…
From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
Ponhvoan Srey, Xiaobao Wu, Cong-Duy Nguyen +3
Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model sig…
Don't Read Everything: A Curvature-Conditioned Query for Linear Attention
Dong Le, Thong Nguyen, Cong-Duy Nguyen +1
Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tas…
WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization
Phong Nam Huu Nguyen, Khoi M. Le, Cong-Duy T Nguyen +3
Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime rem…
When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift
Khoi Le, Tri Cao, Phong Nguyen +5
Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched train-test distributions. Therefore,…
Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Thong Nguyen, Khoi M. Le, Cong-Duy Nguyen +3
Recent advancements in image animation have utilized diffusion models to breathe life into static images. However, existing controllable frameworks typically rely on Lagrangian mot…
Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
Ponhvoan Srey, Xiaobao Wu, Cong-Duy Nguyen +1
Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate unce…
More Bias, Less Bias: BiasPrompting for Enhanced Multiple-Choice Question Answering
Duc Anh Vu, Thong Nguyen, Cong-Duy Nguyen +2
With the advancement of large language models (LLMs), their performance on multiple-choice question (MCQ) tasks has improved significantly. However, existing approaches face key li…
Contextual Representation Flow Across Depth in State-Space and Transformer Architectures
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen +3
State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing. However, their different mechanisms ra…
Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity
Shuai Zhao, Yulin Zhang, Luwei Xiao +7
Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) risk remains largely unexplored.…
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding
Thong Nguyen, Zhiyuan Hu, Xu Lin +3
Recent years have witnessed outstanding advances of large vision-language models (LVLMs). In order to tackle video understanding, most of them depend upon their implicit temporal u…
CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base
Cong-Duy Nguyen, Xiaobao Wu, Duc Anh Vu +3
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal reasoning capabilities, but they remain susceptible to hallucination, particularly object hallucination…
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation
Cong-Duy Nguyen, Xiaobao Wu, Thong Nguyen +5
Previous research on multimodal entity linking (MEL) has primarily employed contrastive learning as the primary objective. However, using the rest of the batch as negative samples…
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Thong Thanh Nguyen, Xiaobao Wu, Yi Bin +3
To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to repre…
Multi-Scale Contrastive Learning for Video Temporal Grounding
Thong Thanh Nguyen, Yi Bin, Xiaobao Wu +4
Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video understanding. To encode video moment…
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation
Shuai Zhao, Xiaobao Wu, Cong-Duy Nguyen +4
Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models (LLMs) and downstream tasks. However, PEFT has been proven vulnerable to malicious attacks.…
Breaking PEFT Limitations: Leveraging Weak-to-Strong Knowledge Transfer for Backdoor Attacks in LLMs
Shuai Zhao, Leilei Gan, Zhongliang Guo +5
Despite being widely applied due to their exceptional capabilities, Large Language Models (LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce targ…