31 citations · 53 across the 15 of their papers we have counts for
18 papers
When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration
Yaxin Gong, Gangyi Zhang, Chongming Gao +7
Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful information or an incorrect answ…
Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts
Chenxiao Fan, Chongming Gao, Gangyi Zhang +8
Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confidence is calibrated. The result…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
Medical Reasoning with Large Language Models: A Survey and MR-Bench
Xiaohan Ren, Chenxiao Fan, Wenyin Ma +4
Large language models (LLMs) have achieved strong performance on medical exam-style tasks, motivating growing interest in their deployment in real-world clinical settings. However,…
Uncertainty-aware Generative Recommendation
Chenxiao Fan, Chongming Gao, Yaxin Gong +3
Generative Recommendation has emerged as a transformative paradigm, reformulating recommendation as an end-to-end autoregressive sequence generation task. Despite its promise, exis…
Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
Ziyi Zhao, Chongming Gao, Yang Zhang +5
Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts. However, these prompts become obsolete when the foundation model is upgraded, necessitat…
MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback
Shihao Cai, Chongming Gao, Haoyan Liu +4
The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in…
Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
Wuqiang Zheng, Yiyan Xu, Xinyu Lin +3
With the rapid and continuous increase in academic publications, identifying high-quality research has become an increasingly pressing challenge. While recent methods leveraging La…
K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang +5
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order…
Fine-grained List-wise Alignment for Generative Medication Recommendation
Chenxiao Fan, Chongming Gao, Wentao Shi +3
Accurate and safe medication recommendations are critical for effective clinical decision-making, especially in multimorbidity cases. However, existing systems rely on point-wise p…
Process-Supervised LLM Recommenders via Flow-guided Tuning
Chongming Gao, Mengyao Gao, Chenxiao Fan +3
While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likeliho…
Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription
Zihao Zhao, Chenxiao Fan, Junlong Liu +5
Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flex…
SPRec: Self-Play to Debias LLM-based Recommendation
Chongming Gao, Ruijun Chen, Shuai Yuan +3
Large language models (LLMs) have attracted significant attention in recommendation systems. Current work primarily applies supervised fine-tuning (SFT) to adapt the model for reco…
Agentic Feedback Loop Modeling Improves Recommendation and User Simulation
Shihao Cai, Jizhi Zhang, Keqin Bao +4
Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has p…
Reinforced Prompt Personalization for Recommendation with Large Language Models
Wenyu Mao, Jiancan Wu, Weijian Chen +3
Designing effective prompts can empower LLMs to understand user preferences and provide recommendations with intent comprehension and knowledge utilization capabilities. Neverthele…
Dual-Phase Accelerated Prompt Optimization
Muchen Yang, Moxin Li, Yongle Li +5
Gradient-free prompt optimization methods have made significant strides in enhancing the performance of closed-source Large Language Models (LLMs) across a wide range of tasks. How…
Large Language Models are Learnable Planners for Long-Term Recommendation
Wentao Shi, Xiangnan He, Yang Zhang +5
Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning capacity b…
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
Zihao Zhao, Yi Jing, Fuli Feng +3
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical infor…