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# Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs
- URL: https://www.stirup.si/faithful-and-complete-llm-assisted-automated-construction-of-hierarchi-6eb95474/
- Published: 2026-10-12T04:02:09.000Z
- Updated: 2026-10-12T04:02:09.000Z
- Description: This analysis explores a novel framework for transforming hardware datasheets into structured knowledge graphs using large language models (LLMs). The approach addresses the limitations of manual inspection and naive LLM processing by introducing a staged pipeline that enhances property faithfulness
- Author: Satish Das
- Tags: Research, Stirup Research

## Overview of the Framework

The paper introduces a method for constructing hierarchical hardware knowledge graphs from hardware datasheets using LLMs. Traditional manual inspection is inefficient due to the complexity and fragmentation of datasheets, which often contain dense information across tables, figures, and text. While LLMs can automate this process, naive one-shot processing fails to extract all relevant specifications, even when the document fits within the model's context window.

## Key Components of the Pipeline

The proposed framework employs a staged design that includes chunk-based property extraction, hierarchical routing, and iterative refinement. This approach reduces the per-query context and maintains traceable intermediate representations. The use of LLMs for information extraction and semantic reasoning ensures that the knowledge graphs are semantically organized and source-grounded.

## Evaluation and Performance

The framework was evaluated on 20 hardware datasheets spanning diverse component types and document lengths. The results show that the proposed method achieves 98.8% property faithfulness compared to 76.1% for the one-shot baseline. Additionally, the framework fully covers 83.6% of the faithful baseline properties, while the baseline only covers 10.5% of the framework's properties. These improvements enhance the reliability and comprehensiveness of engineering information for downstream hardware design and verification.

## Implications and Applications

The framework's ability to extract and organize hardware specifications into structured knowledge graphs has significant implications for hardware design and verification. By reducing the risk of overlooked constraints and incorrect decisions, the framework supports more reliable engineering practices. The use of validated instructions and deterministic local execution ensures that the knowledge graphs are traceable and maintain source provenance.

## Limitations and Considerations

While the framework demonstrates strong performance in property faithfulness and completeness, its effectiveness may depend on the quality and structure of the input datasheets. The approach relies on LLMs for information extraction, which may introduce biases or errors if the training data is not representative of the hardware domain. Additionally, the framework's staged design may increase computational complexity compared to simpler one-shot processing methods.

**Source:** [arXiv AI](https://arxiv.org/abs/2610.12762?ref=stirup.si). Analysis based on publicly available information; no hands-on testing is claimed.