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ARXIV:2604.01532 · INDUSTRIAL AI AGENTS · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01532INDUSTRIAL AI AGENTSSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEAyan Das · Dhaval Patel · arXiv
A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities.
Opportunity summary
Pain A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities. To address this critical gap, we introduce PHMForge, the first comprehensive benchmark specifically designed to evaluate LLM agents…
Large language model (LLM) agents are increasingly deployed for complex tool-orchestration tasks, yet existing benchmarks fail to capture the rigorous demands of industrial domains where incorrect decisions carry significant safety and financial consequences. To…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To enable rigorous evaluation, we construct 65 specialized tools across two MCP servers and implement execution-based evaluators with task-commensurate metrics: MAE/RMSE for regression, F1-score…
Industrial AI Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities.
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10.48550/arXiv.2604.01532A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities.
Abstract
Large language model (LLM) agents are increasingly deployed for complex tool-orchestration tasks, yet existing benchmarks fail to capture the rigorous demands of industrial domains where incorrect decisions carry significant safety and financial consequences. To address this critical gap, we introduce PHMForge, the first comprehensive benchmark specifically designed to evaluate LLM agents on Prognostics and Health Management (PHM) tasks through realistic interactions with domain-specific MCP servers. Our benchmark encompasses 75 expert-curated scenarios spanning 7 industrial asset classes (turbofan engines, bearings, electric motors, gearboxes, aero-engines) across 5 core task categories: Remaining Useful Life (RUL) Prediction, Fault Classification, Engine Health Analysis, Cost-Benefit Analysis, and Safety/Policy Evaluation. To enable rigorous evaluation, we construct 65 specialized tools across two MCP servers and implement execution-based evaluators with task-commensurate metrics: MAE/RMSE for regression, F1-score for classification, and categorical matching for health assessments. Through extensive evaluation of leading frameworks (ReAct, Cursor Agent, Claude Code) paired with frontier LLMs (Claude Sonnet 4.0, GPT-4o, Granite-3.0-8B), we find that even top-performing configurations achieve only 68\% task completion, with systematic failures in tool orchestration (23\% incorrect sequencing), multi-asset reasoning (14.9 percentage point degradation), and cross-equipment generalization (42.7\% on held-out datasets). We open-source our complete benchmark, including scenario specifications, ground truth templates, tool implementations, and evaluation scripts, to catalyze research in agentic industrial AI.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities. To address this critical gap, we introduce PHMForge, the first comprehensive benchmark specifically designed to evaluate LLM agents on Prognostics and Healt...
METHOD
Large language model (LLM) agents are increasingly deployed for complex tool-orchestration tasks, yet existing benchmarks fail to capture the rigorous demands of industrial domains where incorrect decisions carry significant safety and financial consequences. To address this cri...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To enable rigorous evaluation, we construct 65 specialized tools across two MCP servers and implement execution-based evaluators with task-commensurate metrics: MAE/RMSE for regression, F1-score for class...
WHY NOW
Industrial AI Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
we introduce PHMForge, the first comprehensive benchmark specifically designed to evaluate LLM agents on Prognostics and Health Management (PHM) tasks through realistic interactions with domain-specific MCP servers
Explicitly stated in the abstract with clear description of the benchmark's purpose and novelty
partial
Our benchmark encompasses 75 expert-curated scenarios spanning 7 industrial asset classes (turbofan engines, bearings, electric motors, gearboxes, aero-engines) across 5 core task categories
Directly stated in the abstract with specific numbers and categories
partial
we find that even top-performing configurations achieve only 68% task completion
Direct numeric result stated in the abstract from evaluation of leading frameworks
partial
with systematic failures in tool orchestration (23% incorrect sequencing)
Direct numeric result stated in the abstract from evaluation
partial
multi-asset reasoning (14.9 percentage point degradation)
Direct numeric result stated in the abstract from evaluation
partial
cross-equipment generalization (42.7% on held-out datasets)
Direct numeric result stated in the abstract from evaluation
partial
implement execution-based evaluators with task-commensurate metrics: MAE/RMSE for regression, F1-score for classification, and categorical matching for health assessments
Directly stated in the abstract with specific metric details
partial
existing benchmarks fail to capture the rigorous demands of industrial domains where incorrect decisions carry significant safety and financial consequences
Directly stated in the abstract as motivation for creating PHMForge
partial
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A benchmark for evaluating LLM agents in industrial maintenance tasks, revealing significant gaps in current capabilities.
Segment
Industrial AI Agents
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
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next verification path
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stale
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passport absent
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stale
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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