1. Bibliographic Information

1.1. Title

Incident Diagnosing and Reporting System Based on Retrieval Augmented Large Language Model

1.2. Authors

The authors are Peng Yuan, Lu-An Tang, Yanchi Liu, Moto Sato, Haifeng Chen from NEC Labs America, and Kobayashi Yuji from NEC Central Research Lab. The affiliations indicate that this research originates from an industrial research environment (NEC Corporation), suggesting a focus on practical applications and solutions for real-world business problems. The primary authors have previously published work on anomaly detection and incident analysis in IoT systems, indicating deep expertise in the domain.

1.3. Journal/Conference

The paper does not explicitly state the publication venue. However, the provided publication date of April 11, 2025, suggests it is a preprint or a paper submitted to a future conference. The structure of the paper, particularly the "Demo Scenario" section and its short length, strongly implies it was submitted as a demonstration paper for an academic conference. Conferences like ECML/PKDD, where the authors have previously published, are reputable venues for machine learning and data mining research.

1.4. Publication Year

2025 (as per the provided metadata).

1.5. Abstract

The abstract introduces the challenge of maintaining Internet of Things (IoT) systems, where analyzing abnormal sensor data to generate incident reports is a labor-intensive task traditionally handled by domain experts. The paper proposes to automate this process using Large Language Models (LLMs), but highlights two key obstacles: LLMs' lack of background knowledge about specific IoT deployments and their difficulty in understanding the complex relationships between sensors during an incident. To address these issues, the authors present RAIDR (Retrieval Augmented language model based Incident Diagnosing and Reporting system). RAIDR works by retrieving relevant system documents (e.g., manuals, historical reports) based on the features of a new incident. It then uses this retrieved information to augment an LLM, enabling it to analyze anomalies, identify potential root causes, and automatically generate comprehensive incident reports, thereby streamlining system maintenance and troubleshooting.

1.6. Original Source Link

The provided link is /files/papers/6942a53a742e302e037d0543/paper.pdf. This appears to be a local file path, indicating the paper is likely a preprint or an unarchived manuscript. Its official publication status is unknown.

2. Executive Summary

2.1. Background & Motivation

The core problem addressed by this paper is the immense difficulty and inefficiency of diagnosing failures in modern Internet of Things (IoT) systems. These systems can involve hundreds or thousands of sensors, and a single incident can generate a flood of abnormal data. Traditionally, domain experts must manually sift through this data, cross-reference it with system documentation and past incidents, and compile a report—a process that can take weeks. This slow response time hinders timely repair and maintenance.

While Large Language Models (LLMs) offer a promising avenue for automating such knowledge-intensive tasks, they face two major challenges in this context:

1. Background Knowledge Gap: A general-purpose LLM has no inherent knowledge of a specific IoT system's architecture, sensor types, or operational environment.

2. Incident Complexity: IoT incidents are often complex, with a root cause in one component leading to a cascade of anomalies across multiple, interconnected sensors. An LLM needs to understand these dependencies to perform an accurate diagnosis.

   The paper's innovative idea is to bridge this gap not by retraining a massive model, but by augmenting a pre-existing LLM with domain-specific knowledge at the time of inference, using a technique called Retrieval-Augmented Generation (RAG).

2.2. Main Contributions / Findings

The primary contribution of this paper is the proposal and demonstration of RAIDR, a complete system for automated incident diagnosis and reporting in IoT environments. The key contributions are:

1. A Novel RAG-based Framework for IoT: The paper designs a system that synergistically combines incident analysis, document retrieval, and LLM-based report generation. This framework is specifically tailored to the IoT domain.

2. Integration of Graph-based Analysis: RAIDR incorporates a sensor relationship graph to model dependencies and understand how anomalies propagate, addressing the challenge of incident complexity.

3. Automated Report Generation: The system automates the end-to-end process from raw anomaly detection to the generation of a structured, human-readable incident report, complete with suggested actions. This significantly reduces the manual labor required from domain experts.

4. Demonstration of a Practical System: Through a detailed demo scenario, the paper shows the practical viability of the RAIDR system, illustrating its user interface and workflow for analyzing a real-world incident.

   The key finding is that by augmenting an LLM with retrieved contextual documents (like sensor manuals and historical incident tickets), the model can effectively analyze complex IoT incidents and generate useful, formatted reports that facilitate faster decision-making for troubleshooting and maintenance.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

3.1.1. Internet of Things (IoT)

The Internet of Things (IoT) refers to a vast network of interconnected physical objects—from simple sensors and home appliances to industrial machinery and city infrastructure—that are embedded with sensors, software, and other technologies. These "things" can connect to the internet to collect and exchange data. In the context of this paper, IoT systems are used for monitoring applications (e.g., smart cities, healthcare), where a large number of sensors generate continuous streams of data about system behavior and environmental conditions.

3.1.2. Large Language Model (LLM)

A Large Language Model (LLM) is a type of artificial intelligence model built on a deep learning architecture (typically the Transformer architecture) and trained on massive amounts of text and code data. This extensive training enables LLMs like OpenAI's ChatGPT or Meta's Llama 2 to understand, summarize, translate, predict, and generate human-like text. However, their knowledge is limited to the data they were trained on and they often lack specific, real-time, or proprietary information, which is a core problem RAIDR aims to solve.

3.1.3. Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique designed to enhance the capabilities of LLMs by connecting them to external knowledge bases. Instead of relying solely on its internal, pre-trained knowledge, a RAG system follows a two-step process:

1. Retrieval: When given a query or prompt, the system first searches an external data source (e.g., a database of documents, a collection of PDFs, a website) for information relevant to the query. This is often done by converting the query and the documents into numerical representations (embeddings) and finding the documents with the closest embeddings.

2. Generation: The retrieved information is then packaged along with the original query into a new, more detailed prompt. This augmented prompt is fed to the LLM, which uses the provided context to generate a more accurate, detailed, and factually grounded response.

   RAIDR uses RAG to provide the LLM with crucial context, such as sensor manuals and similar historical incident reports.

3.1.4. In-context Learning

In-context Learning is a powerful capability of modern LLMs where the model can learn to perform a task by seeing a few examples (few-shot learning) or instructions within the prompt itself, without requiring any updates to its underlying parameters (i.e., no retraining or fine-tuning). RAIDR leverages this by including examples from historical incident reports in the prompt, guiding the LLM to generate the new report in the correct format and style.

3.2. Previous Works

The paper builds upon prior research in several areas:

• LLMs for System Operations: The authors cite Sarda et al. (2023) and Ahmed et al. (2023), which explored using LLMs for diagnosing anomalies in microservices and cloud incidents. These works established that LLMs, while powerful, need external context to be effective in these specialized domains. Ahmed et al. (2023) specifically proposed a RAG-like approach for recommending root causes in cloud incidents, which is a direct predecessor to the RAIDR concept.
• LLMs for Time-Series Analysis: $Su et al. (2024)$ provides a literature review on the emerging use of LLMs for forecasting and anomaly detection tasks, confirming the trend of applying these models to data types beyond natural language.
• IoT Incident and Anomaly Analysis: Several papers by the same authors (Yuan et al., 2022, 2023) form the foundation for RAIDR's incident analysis module.
  • Yuan et al. (2022) presented systems for detecting anomalies in categorical sensor data using 3D histograms. This likely provides the "detected anomalies" that serve as input to RAIDR.
  • Yuan et al. (2023) introduced a "Temporal Graph Based Incident Analysis System." This work is critical as it describes how to construct the sensor's relationship graph based on correlations and timestamps, which RAIDR uses to understand how anomalies propagate.
• Root Cause Analysis: The paper references several works on root cause analysis in software and cloud systems (Li et al. 2019, Chen et al. 2019, etc.). These traditional methods often rely on statistical analysis or causality inference to pinpoint faulty machines or services. RAIDR differentiates itself by using an LLM to generate qualitative, human-readable reports and hypotheses rather than just identifying a component.

3.3. Technological Evolution

The approach to IoT incident diagnosis has evolved significantly:

1. Manual Analysis: Initially, the process was entirely manual, relying on the experience and time of domain experts.
2. Statistical & ML-based Anomaly Detection: Later, statistical methods and classical machine learning models were developed to automatically detect anomalies in sensor data (Yuan et al. 2022). These systems could flag problems but often could not explain the root cause or provide a holistic incident summary.
3. Graph-based Analysis: To capture the complex interactions, graph-based models were introduced to analyze how failures propagate through a system (Yuan et al. 2023).
4. LLM-based Automation: The current trend, exemplified by RAIDR, is to leverage LLMs for higher-level cognitive tasks. The key innovation is the use of RAG to make general-purpose LLMs "experts" in a specific domain by providing them with the necessary knowledge on-the-fly. This moves beyond simple anomaly flagging to automated reasoning and reporting.

3.4. Differentiation Analysis

RAIDR's core innovation lies in its holistic system integration and its application of RAG to the specific challenges of IoT:

• Compared to Generic LLMs: RAIDR is not just a wrapper around an LLM. It is an engineered system that enriches the LLM with crucial, domain-specific context (sensor graphs, manuals, historical tickets) that a generic model would lack.
• Compared to Traditional Anomaly Detection: While older systems could identify anomalies, RAIDR goes a step further by synthesizing this information, analyzing relationships, and generating a comprehensive narrative of the incident, including potential causes and suggested actions.
• Compared to Other RAG Systems: RAIDR tailors the RAG concept to the unique data types of IoT incidents. The "query" is not just a text string but a complex "incident signature" derived from time-series data and graph structures. This multi-modal approach to generating the retrieval query is a key differentiator.

4. Methodology

4.1. Principles

The core principle of RAIDR is to overcome the inherent knowledge limitations of LLMs by creating a dynamic, context-aware pipeline. The system operates on the premise that an LLM can perform expert-level diagnosis and reporting if it is provided with the right information at the right time. This is achieved through a three-stage process: first, deeply analyze the incident's characteristics; second, retrieve relevant historical and technical documents; and third, synthesize all this information to generate a report.

4.2. Core Methodology In-depth

RAIDR is composed of three interconnected modules that execute in sequence.

4.2.1. Module 1: Incident Analysis

This module processes the raw anomaly data to create a structured, machine-readable representation of the incident.

• Input Data: The system takes two main inputs:

  1. Detected Anomalies: A list of abnormal sensor readings from various anomaly detectors. These detectors could be based on different mechanisms as described in prior work (Yuan et al. 2022).
  2. Sensor Relationship Graph: A pre-computed graph where nodes represent sensors and edges represent dependencies or influences between them. The paper states this graph can be constructed based on the "correlation of the sensor readings or categorical value switch timestamps" (Yuan et al. 2023), allowing the system to model how a fault in one sensor might affect others.

• Processing Steps:

  1. Anomaly Clustering: RAIDR first runs a clustering algorithm to group anomalies affecting related sensors. This helps consolidate scattered alerts into coherent event groups.
  2. Incident Period Identification: The system calculates an "overall anomaly score" over time and uses a threshold (e.g., based on the number of abnormal sensors) to automatically determine the start and end times of a system-level incident.
  3. Incident Signature Generation: For each identified incident, RAIDR generates "incident signatures" or "features to represent the incident timeline and influences." While not explicitly defined, these signatures likely consist of a structured summary including:
     • The list of affected sensors.
     • The timeline and duration of anomalies for each sensor.
     • Key statistics or features of the anomalous data.
     • Graph-based metrics from the subgraph of affected sensors. This signature serves as the query for the next module.

4.2.2. Module 2: Document Retrieval

This module acts as the "memory" of the system, finding relevant information to help diagnose the new incident.

• Knowledge Base: The system maintains a knowledge base containing:
  • Historical Data: Past incident tickets and reports.
  • System Documentation: Technical documents like sensor descriptions and operation manuals.
• Retrieval Process: The "incident signature" generated in the previous module is used as a query to search the knowledge base. The system retrieves the most "similar" historical incident tickets. The paper does not specify the exact retrieval mechanism, but in a typical RAG system, this would involve:
  1. Embedding: Converting the incident signature and the historical tickets into high-dimensional vectors (embeddings) using a suitable model.
  2. Similarity Search: Using a vector database to find the historical tickets whose embeddings are closest (e.g., by cosine similarity) to the new incident's embedding.

4.2.3. Module 3: Report Generation

This is the final stage where the LLM synthesizes all the collected information into a human-readable report.

• Prompt Construction: This is the most critical step. RAIDR automatically constructs a detailed prompt for the LLM. The paper specifies that the prompt includes three essential parts:

  1. Background Knowledge: Information about the relevant sensors, retrieved from system manuals. This tells the LLM what the components are and how they should function.
  2. In-Context Examples: The full text of the similar historical incident reports retrieved in the previous module. This shows the LLM the desired format, tone, and structure of the output report.
  3. New Incident Data: The raw details of the current incident, including error messages, lists of anomalous sensors, and their timelines. This is the primary data to be analyzed.

• LLM Invocation and Report Generation: The user initiates this step (e.g., by clicking "Generate Report"). The constructed prompt is sent to an LLM (such as ChatGPT or a proprietary model like NEC's Cotomi). The LLM processes the rich context and generates a final incident report that follows the historical format. This report includes an analysis of the anomalies, hypotheses about the root cause, and suggested actions for troubleshooting.

• User Interface and Workflow: The process is managed through a dashboard, as shown in Figure 1.

  Figure 1: The incident report dashboard of RAIDR

  The user workflow demonstrated in the paper follows these steps:

  1. The user is presented with a timeline of system anomalies, with incidents automatically highlighted.
  2. Upon selecting an incident, RAIDR displays a list of similar historical tickets it has retrieved (Panel A).
  3. The system visualizes the timeline of the current incident (View B) and allows comparison with a selected historical incident (View C).
  4. The user can click "Generate Prompt" to see the detailed prompt constructed by RAIDR, which integrates background info, historical examples, and current incident data (Panel D).
  5. Finally, the user clicks "Generate Report," and the LLM's output, a structured incident report with root cause hypotheses, is displayed (Panel E).

5. Experimental Setup

As this is a demonstration paper, it does not contain a formal experimental section with quantitative benchmarks. The evaluation is presented as a qualitative walkthrough of a single use case.

5.1. Datasets

The paper mentions using a "real IoT dataset" for the demonstration. However, it does not provide any specific details about the dataset's origin, domain (e.g., smart building, industrial manufacturing), size (number of sensors, duration of data), or characteristics. The dataset presumably contains:

1. Time-series data from a variety of sensors.

2. A historical log of detected anomalies.

3. A repository of previously filed incident tickets, which include human-written reports.

   The choice of a real dataset is appropriate to demonstrate the system's applicability to practical, real-world problems.

5.2. Evaluation Metrics

No quantitative evaluation metrics are used in the paper. The system's effectiveness is demonstrated qualitatively through the "Demo Scenario." The implicit success criterion is the ability of RAIDR to generate a coherent, useful, and properly formatted incident report that can plausibly assist a human operator in troubleshooting. Formal metrics for evaluating the quality of generated text, such as ROUGE or BLEU, or metrics for diagnostic accuracy are not presented.

5.3. Baselines

No baseline models are formally compared against. The implicit baseline is the "traditional" manual process, where domain experts perform the entire diagnosis and reporting task without AI assistance. The paper's primary claim is that RAIDR can automate and accelerate this manual process. A comparison with a "vanilla" LLM (without RAG) is also implied but not experimentally verified.

6. Results & Analysis

The results of the paper are presented entirely through the description of the "Demo Scenario" and the accompanying dashboard screenshot (Figure 1).

6.1. Core Results Analysis

The core result is the successful end-to-end execution of the RAIDR workflow, culminating in the generation of a structured incident report. The analysis of the demo scenario shows that RAIDR successfully performs each of its intended functions:

1. Incident Identification and Visualization: RAIDR effectively processes raw anomalies to identify a coherent incident period and visualizes the system's health over time. This helps the user focus on the most critical events.

2. Contextual Retrieval: The system demonstrates the ability to retrieve relevant historical tickets (listed in Panel A.1 of Figure 1). This is a crucial step, as the context from these tickets informs the final report.

3. Comparative Analysis: By displaying the new incident's timeline (View B) next to a similar historical one (View C), the system provides the user with an intuitive way to compare patterns and validate the retrieved match.

4. Automated Prompt Engineering: RAIDR is shown to automatically assemble all necessary information—background context, historical examples, and current data—into a single, comprehensive prompt (Panel D). This automates a complex task that would otherwise require significant manual effort.

5. Report Generation: The final output (Panel E) is a complete incident report generated by the LLM. This report is the main deliverable of the system and serves as the primary evidence of its effectiveness. It provides a summary, root cause hypotheses, and actionable suggestions, directly addressing the paper's goal of streamlining troubleshooting.

   In summary, the results demonstrate the feasibility and practical utility of the proposed RAG-based system for IoT incident diagnosis.

6.2. Data Presentation (Tables)

There are no data tables presented in this paper.

6.3. Ablation Studies / Parameter Analysis

The paper does not include any ablation studies. An ablation study would be valuable to quantify the contribution of each component of RAIDR. For example, experiments could be run to compare the quality of generated reports under different conditions:

• LLM with no RAG (only new incident data).

• LLM + RAG with only system documentation (no historical tickets).

• LLM + RAG with only historical tickets (no documentation).

• The full RAIDR system.

  Such studies would provide stronger evidence for the necessity of each component in the RAIDR framework. No parameter analysis (e.g., regarding the number of retrieved documents) is discussed.

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper presents RAIDR, a novel system that leverages a Retrieval-Augmented Generation (RAG) approach to automate incident diagnosis and reporting for IoT applications. By analyzing incident signatures, retrieving relevant historical tickets and system documentation, and using this context to prompt an LLM, RAIDR can automatically generate comprehensive incident reports. The system is designed to overcome the key limitations of applying LLMs in specialized domains, namely their lack of contextual knowledge. The authors conclude that RAIDR can effectively streamline troubleshooting and maintenance operations for a wide variety of IoT systems.

7.2. Limitations & Future Work

The paper, being a short demonstration proposal, has several limitations which also point to future research directions:

• Lack of Quantitative Evaluation: The primary limitation is the absence of any rigorous, quantitative evaluation. The quality of the generated reports is not measured, nor is the accuracy of the root cause hypotheses. Future work should involve user studies or quantitative benchmarks (e.g., comparing RAIDR's output against reports from human experts) to validate its effectiveness.
• Algorithmic Ambiguity: The paper describes the system at a high level, omitting details about the specific algorithms used for key steps like "incident signature" generation and document retrieval (e.g., embedding models, similarity metrics). Future research could explore and compare different techniques for these modules.
• Scalability and Performance: The paper does not discuss the computational cost or latency of the system. For real-time incident response, the performance of the retrieval and generation steps would be a critical factor.
• Validation of LLM Output: The system generates "hypotheses" for root causes, but does not include a mechanism to verify them. There is a risk of LLM "hallucination," where the model might generate plausible but incorrect information. Future work could focus on developing methods to automatically validate the LLM's claims against system data.

7.3. Personal Insights & Critique

• Strengths:

  • Practical and High-Value Application: The paper addresses a genuine and costly pain point in IoT operations. The proposed solution is practical and has clear business value.
  • Sound Architectural Design: The choice of a RAG architecture is perfectly suited to the problem. Instead of attempting the expensive and difficult task of fine-tuning an LLM for every IoT deployment, RAIDR uses a more flexible and scalable approach by injecting knowledge at inference time.
  • Synergistic Integration: The system's strength lies in its integration of multiple techniques: time-series analysis, graph modeling, information retrieval, and large language models. This holistic approach is more powerful than any single component in isolation.

• Critique and Areas for Improvement:

  • Oversimplification of "Root Cause Identification": The paper claims the system identifies root causes. It is more accurate to say it generates plausible root cause hypotheses. This distinction is crucial, as the output of an LLM still requires verification by a human expert. The system is best viewed as a powerful "expert assistant" rather than a fully autonomous diagnostician.
  • Contribution is in Application, Not Novel Theory: The paper's novelty lies in the application and integration of existing technologies (RAG, LLMs) to a specific domain (IoT), rather than in proposing a new fundamental algorithm. While valuable, its scientific contribution is that of an application paper.
  • Generalizability: The framework seems generalizable to other complex system monitoring domains, such as AIOps (AI for IT Operations), network management, and industrial process control. This potential for broader application is a significant strength that could be further explored. Future work could test RAIDR's effectiveness in these related fields.