AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance

arXiv:2506.03828 · cs.AI, cs.MA · Submitted 2025-06-04 · Read on arXiv

cs.AI, cs.MA

Submitted: 2025-06-04

Updated: 2026-08-30

Terminology

• Benchmark — 46× in this paper · explained in 2 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• Benchmarking — 22× in this paper · explained in 3 episodes
• Failure Modes — 17× in this paper · explained in 3 episodes
• Verification — 15× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• AI Agents — 9× in this paper · explained in 2 episodes
• Multi-Agent System — 9× in this paper · explained in 6 episodes
• Trace — 8× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Large Language Model — 6× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Predictive Maintenance — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Agent Architectures — 3× in this paper · explained in 1 episode
• Evaluation Dimensions — 3× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Agentic AI — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Failure Mode and Effects Analysis — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Reasoning Traces — 2× in this paper · explained in 3 episodes
• Small Language Models — 2× in this paper
• alpha — 2× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Agent Interactions — 1× in this paper · explained in 1 episode
• Building Management Systems — 1× in this paper
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Directed Acyclic Graph — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• ARE: Scaling Up Agent Environments and Evaluations
• Small Language Models are the Future of Agentic AI
• AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
• Towards Enterprise-Ready Computer Using Generalist Agent
• MLGym: A New Framework and Benchmark for Advancing AI Research Agents
• AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories
• From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
• Large Action Models: From Inception to Implementation
• MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
• AgentRM: Enhancing Agent Generalization with Reward Modeling
• $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
• ActionStudio: A Lightweight Framework for Data and Training of Large Action Models

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection