most citedFrom Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents

1 citations · 1 across the 7 of their papers we have counts for

collaborators
Showing cs.AIShow all

8 papers · 1 filter

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI20261 cited

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,…

cs.AI2026

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…

cs.AI2025

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…