Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance
Quick Answer
The study introduces CoffeeBench, a fault diagnostics task for LLM agents, revealing that while LLMs struggle to identify appliance faults from sensor data, the best models can refine their diagnoses iteratively.
Quick Take
Key insights include the importance of initial diagnostic uncertainty and reasoning soundness as predictors of success, alongside practical recommendations for enhancing model performance.
Key Points
- CoffeeBench evaluates on diagnosing faults in a simulated appliance.
- Best-performing LLMs can iteratively refine fault diagnoses despite initial struggles.
- Initial diagnostic uncertainty is a high-signal predictor of diagnostic success.
- Practical insights include tracking episode state to reduce timeouts.
- Deliberation loops can enhance the refinement of diagnostic hypotheses.
DeepSignal Analysis
What happened
The study presents CoffeeBench, a diagnostic task for LLM agents focused on identifying faults in a simulated appliance. While LLMs generally struggle with this task, the most effective models can refine their diagnoses through iterative processes. Key factors influencing success include initial diagnostic uncertainty and reasoning soundness.
Key evidence
- CoffeeBench is introduced as a fault diagnostics task for LLM agents, utilizing a simulated appliance to evaluate their performance.
- The evaluated LLMs have difficulty consistently identifying faults from sensor data, indicating limitations in their diagnostic capabilities.
- The study identifies initial diagnostic uncertainty and the soundness of reasoning as significant predictors of diagnostic success for LLMs.
Why it matters
This research highlights the challenges faced by LLMs in practical industrial applications, particularly in fault diagnosis. Understanding these limitations is crucial for improving model performance and integrating LLMs into real-world industrial workflows. The findings suggest that enhancing diagnostic strategies could lead to better automation in asset management.
Paper Resources
📖 Reader Mode
~2 min readAbstract:In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced visibility and automation. Prognostics and health management (PHM) and natural language processing (NLP) practitioners alike have explored the application of large language models (LLMs) to industrial asset management, but many prior works' focus on exam-like benchmarks has limited their assessment of LLMs' ability to automate practical operations. Herein, we introduce CoffeeBench, a fault diagnostics task for LLM agents, using a simulated appliance. The LLMs we evaluate struggle to consistently identify faults from the sensor information alone, but the best-performing ones can iteratively refine the fault-option space to eventually obtain a successful diagnosis. Our subsequent analyses, therefore, focus on: differentiating models by diagnostic efficiency (RQ1); examining the lagging performance of the three worst models (RQ2); and identifying viable heuristics for selective prediction (RQ3), including that initial diagnostic uncertainty and LLM-judged soundness of reasoning are high-signal predictors of diagnostic success. In addition to our benchmark results (Section 3.1), we provide practical insights for improving the performance of the open-weight models on CoffeeBench, such as by explicitly tracking episode state on their behalf to reduce timeouts (Section 3.2) or applying a deliberation loop to refine diagnostic hypotheses (Section 3.3).
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19291 [cs.CL] |
| (or arXiv:2610.19291v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19291 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lucas McCabe [view email]
[v1]
Tue, 13 Oct 2026 23:12:32 UTC (337 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →The "10th Juror": Open-Set Standpoint Screening for Bureaucratic Bias Detection
MARS-Gov introduces a framework for detecting bureaucratic bias in Dutch government documents, achieving a new state-of-the-art F1 score of 0.880. This model outperforms existing zero-shot detectors by 20.2 points and reduces unnecessary interventions to just 2.5%. The framework's dynamic '10th juror' adapts to emerging biases, enhancing legal language processing.