13 citations · 15 across the 13 of their papers we have counts for
13 papers
SAGE: A Hierarchical Framework for Evaluating Interpretive Literary Quality in Narratives
Tianyu Wang, Nianjun Zhou
Assessing the literary quality of narratives requires evaluating interpretive dimensions (cultural representation, emotional depth, and philosophical engagement) that existing NLG…
DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules
Devin Yasith De Silva, Dhaval Patel, Christodoulos Constantinides +7
Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perform corrective actions. The bo…
Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge
Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula +4
Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit…
SAGE: Hierarchical LLM-Based Literary Evaluation through Ontology-Grounded Interpretive Dimensions
Tianyu Wang, Nianjun Zhou
Evaluating literary quality requires assessing interpretive dimensions such as cultural representation, emotional depth, and philosophical sophistication that resist straightforwar…
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
Natalia Martinez Gil, Fearghal O'Donncha, Wesley M. Gifford +3
We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additio…
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko +6
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval,…
Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
Fearghal O'Donncha, Nianjun Zhou, Natalia Martinez +4
Industrial maintenance platforms contain rich but fragmented evidence, including free-text work orders, heterogeneous operational sensors or indicators, and structured failure know…
Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam +2
We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear…
Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information
Christodoulos Constantinides, Shuxin Lin, Nianjun Zhou +1
This paper presents a novel multi-agent system called Chat-of-Thought, designed to facilitate the generation of Failure Modes and Effects Analysis (FMEA) documents for industrial a…
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Dhaval Patel, Shuxin Lin, James Rayfield +7
AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling, to minimize system downtime. W…
CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education
Tianyu Wang, Nianjun Zhou, Zhixiong Chen
Many non-traditional students in cybersecurity programs often lack access to advice from peers, family members and professors, which can hinder their educational experiences. Addit…
Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM
Nianjun Zhou, Dhaval Patel, Shuxin Lin +1
This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Lan…
Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
Tianyu Wang, Nianjun Zhou, Zhixiong Chen
Large language models (LLMs) and prompt engineering hold significant potential for advancing computer programming education through personalized instruction. This paper explores th…