5 citations · 10 across the 7 of their papers we have counts for
11 papers
AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment
Yinzhu Quan, Zefang Liu, Frederick Benaben +1
We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chain networks. The framework ena…
EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data
Yinzhu Quan, Zefang Liu
Web agents often revisit the same sites, yet most evaluations discard the procedures learned in earlier successful interactions. We introduce EconSkills, a skill library and evalua…
Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
Zefang Liu, Nam H. Nguyen, Yinzhu Quan +1
Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level r…
CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation
Yinzhu Quan, Xinrui Li, Ying Chen
In e-commerce private-domain channels such as instant messaging and e-mail, merchants engage customers directly as part of their Customer Relationship Management (CRM) programmes t…
EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments
Zefang Liu, Yinzhu Quan
We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The benchmark comprises 360 curated tas…
Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment
Yinzhu Quan, Yujia Xu, Guanlin Chen +2
The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networ…
Retrieval of Temporal Event Sequences from Textual Descriptions
Zefang Liu, Yinzhu Quan
Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking cr…
TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Zefang Liu, Yinzhu Quan
Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce. In this pap…
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
Yinzhu Quan, Zefang Liu
Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory manage…
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
Yinzhu Quan, Zefang Liu
In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms o…
Deep Reinforcement Learning based Group Recommender System
Zefang Liu, Shuran Wen, Yinzhu Quan
Group recommender systems are widely used in current web applications. In this paper, we propose a novel group recommender system based on the deep reinforcement learning. We intro…