6 citations · 25 across the 20 of their papers we have counts for
25 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
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…
StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
Zefang Liu, Chenyang Zhu, Sangwoo Cho +3
Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We p…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Sangwoo Cho, Kushal Chawla, Pengshan Cai +4
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, an…
ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models
Zefang Liu, Chenyang Zhu, Sangwoo Cho +1
Semi-supervised learning in automatic speech recognition (ASR) typically relies on pseudo-labeling, which often suffers from confirmation bias and error accumulation due to noisy s…
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…
AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding
Arman Anwar, Zefang Liu
Traditional cybersecurity tabletop exercises (TTXs) provide valuable training but are often scripted, resource-intensive, and difficult to scale. We introduce AgentBnB, a browser-b…
AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation
Zefang Liu, Arman Anwar
Incident response (IR) requires fast, coordinated, and well-informed decision-making to contain and mitigate cyber threats. While large language models (LLMs) have shown promise as…
A Generalized Multinodal Model for Plasma Particle and Energy Transport
Zefang Liu, Weston M. Stacey
We present a generalized multinodal model for simulating particle and energy transport in toroidal plasma configurations, developed to support burning plasma analysis and reactor-s…
Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
Zefang Liu, Weston M. Stacey
Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region,…
Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations
Zefang Liu, Weston M. Stacey
Achieving controlled burning plasma in tokamaks requires precise regulation of external particle and energy sources to reach and maintain target core densities and temperatures. Th…
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…
Multi-Agent Collaboration in Incident Response with Large Language Models
Zefang Liu
Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language…
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…
Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics
Zefang Liu, Weston M. Stacey
The dynamics of burning plasmas in tokamaks are crucial for advancing controlled thermonuclear fusion. This study applies the NeuralPlasmaODE, a multi-region multi-timescale transp…
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…
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
Zefang Liu, Jiahua Luo
We introduce AdaMoLE, a novel method for fine-tuning large language models (LLMs) through an Adaptive Mixture of Low-Rank Adaptation (LoRA) Experts. Moving beyond conventional meth…
Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis
Zefang Liu, Weston M. Stacey
In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale…
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Zefang Liu
In this paper, we introduce SecQA, a novel dataset tailored for evaluating the performance of Large Language Models (LLMs) in the domain of computer security. Utilizing multiple-ch…
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches
Zefang Liu, John Buford
Anomaly detection in command shell sessions is a critical aspect of computer security. Recent advances in deep learning and natural language processing, particularly transformer-ba…
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…