2 citations · 3 across the 8 of their papers we have counts for
9 papers
PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants
Weimin Lyu, Chen Luo, Guangrui Li +9
Shopping assistants are shifting from ranked product lists toward structured decision support, where systems must synthesize shopper context, product evidence, and next-step guidan…
Sequential statistical inference for Large Language Models: Representation, validity, and monitoring
Yao Xie
This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness. In deployment, LLM systems are queried repeatedly, conditioned on evol…
DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
Yang Zhou, Can Jin, Zihan Dong +7
Reinforcement learning improves the reasoning ability of large language models but remains costly and sample-inefficient, as many rollouts provide weak learning signals. Difficulty…
The World Won't Stay Still: Programmable Evolution for Agent Benchmarks
Guangrui Li, Yaochen Xie, Yi Liu +11
LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks eva…
AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
Yuxuan Lu, Ting-Yao Hsu, Hansu Gu +9
A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications. Yet, traditional A/B testing remains constrained by its dependen…
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data
Yuxuan Lu, Jing Huang, Yan Han +9
Recent research shows that LLM Agents can generate ``believable'' human behaviors via prompt-only methods, and such agents have been increasingly adopted in downstream applications…
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
Haoyu Han, Yaochen Xie, Hui Liu +8
Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understandi…
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Ran Xu, Hui Liu, Sreyashi Nag +8
Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-pur…
Exploring Query Understanding for Amazon Product Search
Chen Luo, Xianfeng Tang, Hanqing Lu +12
Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique chara…