3 citations · 5 across the 11 of their papers we have counts for
15 papers
ArcFlow: Training Flow Policies for Multiple Solver Budgets
Hieu Tran, Hong Yu
Flow-matching policies adjust inference cost through the number of solver steps, but reinforcement learning at a single fixed solver budget can hide degradation at cheaper deployme…
Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
Hieu Tran, Hong Yu
Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense. Yet collusion is defined by enforcement: a cartel, in which competitors shar…
Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
Hieu Tran, Zonghai Yao, Sunjae Kwon +1
Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal. We use reward disagreement…
Second Thoughts: Exploring and Planning through Comparative Imagination in World Models
Hieu Tran, Hong Yu
World models let agents compare imagined outcomes before acting, but an agent must still decide which alternatives to explore and when a proposed plan should replace its learned po…
Chatbot To Help Patients Understand Their Health
Won Seok Jang, Hieu Tran, Manav Mistry +7
Patients must possess the knowledge necessary to actively participate in their care. We present NoteAid-Chatbot, a conversational AI that promotes patient understanding via a novel…
Exploiting Tree Structure for Credit Assignment in RL Training of LLMs
Hieu Tran, Zonghai Yao, Hong Yu
Reinforcement learning improves LLM reasoning, yet sparse delayed reward over long sequences makes token-level credit assignment the key bottleneck. We study the verifiable-reward…
PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning
Hieu Tran, Zonghai Yao, Nguyen Luong Tran +5
Inspired by the dual-process theory of human cognition from \textit{Thinking, Fast and Slow}, we introduce \textbf{PRIME} (Planning and Retrieval-Integrated Memory for Enhanced Rea…
MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
Hieu Tran, Zonghai Yao, Won Seok Jang +4
Generative AI has demonstrated strong potential in healthcare, from clinical decision support to patient-facing chatbots that improve outcomes. A critical challenge for deployment…
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study
Won Seok Jang, Sharmin Sultana, Zonghai Yao +4
OpenNotes gives patients access to their EHR notes, but dense medical jargon limits comprehension. We evaluate closed-source and open-source LLMs for extracting and prioritizing th…
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
Hieu Tran, Zonghai Yao, Junda Wang +3
This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and fa…
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
Hieu Tran, Junda Wang, Yujan Ting +2
Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularl…
MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis
Junda Wang, Yujan Ting, Zonghai Yao +5
Medical multimodal large language models (MLLMs) can perform well on existing medical visual question answering (MedVQA) benchmarks, but their training data often does not match cl…
ReadCtrl: Personalizing text generation with readability-controlled instruction learning
Hieu Tran, Zonghai Yao, Lingxi Li +1
Content generation conditioning on users's readability is an important application for personalization. In an era of large language models (LLMs), readability-controlled text gener…
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei +6
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Recor…
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
Hieu Tran, Zhichao Yang, Zonghai Yao +1
To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its…