# KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis

arXiv: https://arxiv.org/abs/2602.10246 · HTML: https://mlsanity.com/paper/2602.10246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 90 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 15 of 15)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [LogLLM: Log-based Anomaly Detection Using Large Language Models](https://mlsanity.com/paper/2411.08561/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [The Vision of Autonomic Computing: Can LLMs Make It a Reality?](https://mlsanity.com/paper/2407.14402/) (2024)
- [KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks](https://mlsanity.com/paper/2402.13264/) (2024)
- [Arcus: SLO Management for Accelerators in the Cloud with Traffic Shaping](https://mlsanity.com/paper/2410.17577/) (2024)
- [Knowledge-Driven Hybrid SSD Management Enhanced by Fine-Tuned LLMs](https://mlsanity.com/paper/2503.13105/) (2025)
- [Device-Level Optimization Techniques for Solid-State Drives: A Survey](https://mlsanity.com/paper/2507.10573/) (2025)

## Built on by (most cited first, 2 of 2)

- [VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models](https://mlsanity.com/paper/2604.03956/) (2026)
- [REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPC](https://mlsanity.com/paper/2610.19222/) (2026)

## Abstract (arXiv)

Solid State Drives (SSDs) are critical to datacenters, consumer platforms, and mission-critical systems. Yet diagnosing their performance and reliability is difficult because data are fragmented and time-disjoint, and existing methods demand large datasets and expert input while offering only limited insights. Degradation arises not only from shifting workloads and evolving architectures but also from environmental factors such as temperature, humidity, and vibration. We present KORAL, a knowledge driven reasoning framework that integrates Large Language Models (LLMs) with a structured Knowledge Graph (KG) to generate insights into SSD operations. Unlike traditional approaches that require extensive expert input and large datasets, KORAL generates a Data KG from fragmented telemetry and integrates a Literature KG that already organizes knowledge from literature, reports, and traces. This turns unstructured sources into a queryable graph and telemetry into structured knowledge, and both the Graphs guide the LLM to deliver evidence-based, explainable analysis aligned with the domain vocabulary and constraints. Evaluation using real production traces shows that the KORAL delivers expert-level diagnosis and recommendations, supported by grounded explanations that improve reasoning transparency, guide operator decisions, reduce manual effort, and provide actionable insights to improve service quality. To our knowledge, this is the first end-to-end system that combines LLMs and KGs for full-spectrum SSD reasoning including Descriptive, Predictive, Prescriptive, and What-if analysis. We release the generated SSD-specific KG to advance reproducible research in knowledge-based storage system analysis. GitHub Repository: https://github.com/Damrl-lab/KORAL