244 citations · 1.3k across the 96 of their papers we have counts for
106 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…
SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents
Shangyang Wu, Shuai Zhao, Ziyue Zhu +3
Training long-horizon agents to solve complex tasks requires effective supervision over extended interaction sequences. However, sparse terminal rewards obscure intermediate contri…
Practical Secrets Extraction against Black-box LLMs
Shiqian Zhao, Siwei Jiang, Xinfeng Li +5
Large language models (LLMs) increasingly power autonomous coding agents such as Codex and Claude Code, yet their training corpora may contain confidential credentials exposed in p…
TIEM: Temporal Integration of Hypergraph Evidence and Skill Memory for Event-Driven Financial Forecasting
Wenjin Liu, Shen Pang, Chenxi Wang +6
Event-driven catalyst-outcome forecasting increasingly uses retrieval- and memory-augmented large language model agents for prediction. However, training-data contamination and tem…
Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles
Mingzhe Du, Anh Tuan Luu, Dong Huang +1
Benchmarks for LLM-generated GPU kernels decide correctness with a few random inputs and a loose floating-point tolerance, and their verdicts now feed leaderboards and reinforcemen…
Diachronic Hypergraphs for Orchestrated Multi-Agent Multimodal Memory Curation
Yichao Feng, Ran Zhang, Haoran Luo +3
Multi-agent systems solve tasks through collaboration, tool use, multimodal reasoning, and orchestration, but each agent operates within a knowledge boundary defined by its observa…
CataOPD: Catalytic On-Policy Distillation for Large Language Model Reasoning
Wenjin Liu, Chenxi Wang, Jiapu Wang +3
Reinforcement learning (RL) and on-policy distillation (OPD) are two representative paradigms for improving large language model reasoning. However, when no correct trajectory is s…
AdaPilot: Towards Scene-Adaptive Policy Learning for Cross-Generator Text-to-Image Quality Optimization
Wenjin Liu, Fayuan Ke, Yue Lu +3
Existing methods for improving text-to-image generation quality have progressed from generator fine-tuning and prompt optimization to reinforcement learning with multi-turn visual…
MMDynOpt-Agent: Dynamic Optimization for Multimodal Large Language Model Reasoning via Reinforcement Learning
Wenjin Liu, Haoran Luo, Fayuan Ke +5
Recently, multimodal large language models (MLLMs) have demonstrated strong potential in visual understanding and complex reasoning tasks. However, existing methods often struggle…
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…
PowerAtlas: Feasibility-Aware Language Models for Electricity-Computing Co-Scheduling
Kaiwen Jiang, Siya Xu, Ziyue Zhu +4
Electricity-computing co-scheduling (ECCS) decides where and when computing tasks run together with how generators are dispatched, because task placement changes nodal demand while…
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…
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
Activation Steering Induces Emergent Misalignment: Steering Vector Construction and Comprehensive Evaluation
Qi Cao, Jian Lou, Meiting Liu +4
Activation steering (AS) has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). AS modulates model behavior in a flexible an…
GRACE: Step-Level Benchmark for Faithful Reasoning over Context
Hoang Pham, Dong Le, Anh Tuan Luu
Many reasoning tasks require models to reason over input context, from document-grounded question answering to rule-based deduction. Chain-of-Thought (CoT) prompting produces trace…
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,…
Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization
Huimin Xu, Shuai Zhao, Xiaobao Wu +1
Reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving the reasoning ability of large language models. However, widely used RLVR algor…
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
Tri Cao, Khoi Le, Thong Nguyen +7
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…
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…
OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis
Kanzhi Cheng, Zehao Li, Zheng Ma +11
Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achieving a marked performance leap…
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
Ponhvoan Srey, Quang Minh Nguyen, Xiaobao Wu +1
Uncertainty estimation (UE) aims to quantify uncertainty in large language model (LLM) outputs to flag potential hallucinations and support reliable deployment. However, UE metrics…