Pith. sign in

REVIEW 1 major objections 46 references

SafeGen uses an LLM and Hyper Knowledge Graph to generate design-aware assertions for semantic fault criticality assessment in functional safety.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-25 20:28 UTC pith:X3TK2CBN

load-bearing objection SafeGen ties LLMs to an FMEDA-derived HyperKG for traceable assertion generation and semantic fault grading, but the abstract gives no numbers to back the quality claims. the 1 major comments →

arxiv 2606.25296 v1 pith:X3TK2CBN submitted 2026-06-24 cs.AR

SafeGen: LLM-Driven Assertion Generation and Fault Criticality Evaluation for Functional Safety

Xuanyi Tan , Arjun Chaudhuri , Rubin Parekhji , Krishnendu Chakrabarty This is my paper
classification cs.AR
keywords functional safetyassertion generationlarge language modelsfault criticalityknowledge graphFMEDAformal verificationautomotive chips
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SafeGen as an LLM-driven framework assisted by formal verification for assessing fault criticality in automotive chip designs. It builds a document-level Hyper Knowledge Graph incorporating FMEDA guidelines and RTL information to extract verifiable specifications and generate Functional Safety Assertions that are linked to those specs. This allows traceable reasoning and semantic-level grading of faults using formal property verification instead of conservative simulation-based methods. A sympathetic reader would care because accurate fault analysis is essential for safety certification in autonomous systems, where over-conservative assessments can lead to unnecessary design changes.

Core claim

SafeGen leverages large language models and a document-level Hyper Knowledge Graph that incorporates Failure Modes, Effects, and Diagnostic Analysis guidelines to extract verifiable specifications from design and safety documents. The graph is enriched with register-transfer-level information to generate Functional Safety Assertions that are semantically grounded and design-aware. Each assertion is linked to its corresponding specification for traceability. A gate-to-RTL fault-mapping mechanism with formal property verification enables semantic-level fault criticality grading based on assertion violations, demonstrated on a field-oriented control system co-simulation platform.

What carries the argument

The document-level Hyper Knowledge Graph (HyperKG) enriched with FMEDA guidelines and RTL data, which guides LLM generation of specification-linked Functional Safety Assertions (FSAs) for traceable fault assessment.

Load-bearing premise

The document-level Hyper Knowledge Graph accurately incorporates FMEDA guidelines and RTL information to produce verifiable specifications and design-aware Functional Safety Assertions.

What would settle it

Demonstration that assertions produced by SafeGen either miss critical faults affecting system safety or incorrectly grade faults due to inaccurate HyperKG enrichment from the documents.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Generates higher-quality assertions than existing LLM-based frameworks.
  • Provides greater semantic interpretability in fault criticality assessment than traditional simulation.
  • Supports evaluation of both stuck-at and bridging faults via gate-to-RTL mapping.
  • Enables traceable reasoning throughout the assessment process.
  • Validated on a digital-physical co-simulation platform for a field-oriented control system.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be adapted for other safety-critical domains like medical devices or aerospace electronics.
  • Semantic traceability may reduce the time required for safety case documentation and certification.
  • Further integration with simulation tools could create hybrid analysis workflows that combine semantic and quantitative insights.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The manuscript presents SafeGen, an LLM-driven framework for functional safety-oriented fault criticality assessment in automotive chip design. It constructs a document-level Hyper Knowledge Graph (HyperKG) that incorporates FMEDA guidelines to extract verifiable specifications from design and safety documents, enriches it with RTL information to generate specification-linked Functional Safety Assertions (FSAs), applies gate-to-RTL fault mapping for stuck-at and bridging faults, and uses formal property verification (FPV) for semantic-level grading of fault criticality. Validation occurs via a digital-physical co-simulation platform on a field-oriented control (FOC) system. The central claim is that SafeGen produces higher-quality assertions than existing LLM-based frameworks and greater semantic interpretability than traditional simulation-based methods.

Significance. If the experimental results hold, the framework could improve functional safety analysis by replacing overly conservative module-level simulation with traceable, design-aware assertions that link faults directly to system-level specifications. The emphasis on semantic interpretability and specification traceability addresses a practical gap in automotive safety workflows.

major comments (1)
  1. [Abstract] Abstract: The claim that 'Experimental results demonstrate that SafeGen generates higher-quality assertions than existing LLM-based assertion generation frameworks while providing greater semantic interpretability in fault criticality assessment compared with traditional simulation-based approaches' is unsupported by any quantitative metrics, baseline descriptions, dataset details, or error analysis in the provided text. This absence makes the central experimental claim impossible to evaluate.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address the concern point-by-point below and will revise the manuscript accordingly to strengthen the presentation of our experimental claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'Experimental results demonstrate that SafeGen generates higher-quality assertions than existing LLM-based assertion generation frameworks while providing greater semantic interpretability in fault criticality assessment compared with traditional simulation-based approaches' is unsupported by any quantitative metrics, baseline descriptions, dataset details, or error analysis in the provided text. This absence makes the central experimental claim impossible to evaluate.

    Authors: We agree that the abstract, in its current form, presents a high-level summary of the results without embedding the supporting quantitative details. The full manuscript contains dedicated experimental sections that include quantitative metrics (e.g., assertion correctness, coverage, and relevance scores), baseline comparisons against prior LLM-based frameworks, dataset descriptions from the FOC system and FMEDA documents, and error analysis. To make the central claim directly evaluable from the abstract itself, we will revise the abstract to concisely incorporate key quantitative highlights, baseline names, and dataset references while preserving its length constraints. This revision will be made in the next version of the manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes an engineering pipeline (LLM + document-level HyperKG enriched with FMEDA guidelines and RTL data to generate linked FSAs, followed by gate-to-RTL fault mapping and FPV) whose outputs are validated experimentally via co-simulation on an FOC system. No equations, fitted parameters, or quantitative predictions appear; claims of higher-quality assertions rest on direct comparison to baselines rather than any reduction of results to self-referential inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked. The derivation is therefore self-contained as a traceable methodology without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Abstract-only review prevents full audit; framework introduces HyperKG and FSAs as core components without external validation of their construction accuracy.

axioms (1)
  • domain assumption FMEDA guidelines provide a reliable basis for extracting verifiable safety specifications from design documents
    Invoked when the HyperKG is built to incorporate FMEDA guidelines
invented entities (2)
  • Hyper Knowledge Graph (HyperKG) no independent evidence
    purpose: Store and link FMEDA guidelines with RTL information to guide FSA generation
    New structure introduced in the framework description
  • Functional Safety Assertions (FSAs) no independent evidence
    purpose: Semantically grounded, traceable assertions for fault criticality evaluation
    Generated component central to the assessment process

pith-pipeline@v0.9.1-grok · 5781 in / 1359 out tokens · 29296 ms · 2026-06-25T20:28:05.237733+00:00 · methodology

0 comments
read the original abstract

With advances in autonomous driving and electric vehicle technologies, functional safety has become a critical requirement in automotive chip design. Traditional simulation-based fault analysis is often overly conservative at the module level and fails to accurately reflect fault criticality. This paper presents SafeGen, an LLM-driven, formal-verification-assisted framework for functional-safety-oriented fault criticality assessment. SafeGen leverages large language models (LLMs) and a document-level Hyper Knowledge Graph (HyperKG) that incorporates Failure Modes, Effects, and Diagnostic Analysis (FMEDA) guidelines to extract verifiable specifications from design and safety documents and evaluate their relevance to overall system safety. The HyperKG is further enriched with register-transfer-level (RTL) information to guide the generation of Functional Safety Assertions (FSAs) that are both semantically grounded and design-aware. Each assertion is linked to its corresponding specification, enabling traceable reasoning throughout the assessment process. A gate-to-RTL fault-mapping mechanism supporting both stuck-at and bridging faults, combined with formal property verification (FPV), enables semantic-level fault criticality grading based on specification-linked assertion violations. A digital-physical co-simulation platform for a field-oriented control (FOC) system is developed to validate SafeGen. Experimental results demonstrate that SafeGen generates higher-quality assertions than existing LLM-based assertion generation frameworks while providing greater semantic interpretability in fault criticality assessment compared with traditional simulation-based approaches.

Figures

Figures reproduced from arXiv: 2606.25296 by Arjun Chaudhuri, Krishnendu Chakrabarty, Rubin Parekhji, Xuanyi Tan.

Figure 1
Figure 1. Figure 1: The difference between fault simulation and formal [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Assertion generation and gate-level fault criticality evaluation. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Alignment between document HyperKG and RTL [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: FOC design for driving a PMSM [41] [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Closed-loop simulation of the FOC system. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Fault criticality distribution of bridging faults (a) [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

46 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    IEEE Draft Standard for Fault Accounting and Coverage Reporting to Digital Modules (FACR).IEEE P1804/D1.7, September 2016(2016), 1–76

    2016. IEEE Draft Standard for Fault Accounting and Coverage Reporting to Digital Modules (FACR).IEEE P1804/D1.7, September 2016(2016), 1–76. SafeGen: LLM-Driven Assertion Generation and Fault Criticality Evaluation for Functional Safety Conference’17, July 2017, Washington, DC, USA

    2016

  2. [2]

    ISO 26262: Road vehicles — Functional safety

    2018. ISO 26262: Road vehicles — Functional safety. Second edition, Parts 1–12

    2018

  3. [3]

    Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, and Csaba Szepesvari

  4. [4]

    To believe or not to believe your llm: Iterative prompting for estimating epistemic uncertainty.Advances in Neural Information Processing Systems37 (2024), 58077–58117

    2024

  5. [5]

    Abramovici, B

    M. Abramovici, B. Krishnamurthy, R. Mathews, B. Rogers, M. Schulz, and S. Seth

  6. [6]

    In1988 IEEE International Test Conference (ITC)

    What is the Path to Fast Fault Simulation?. In1988 IEEE International Test Conference (ITC). IEEE, 10–17

  7. [7]

    Dinesh Reddy Ankireddy, Sudipta Paria, Aritra Dasgupta, Sandip Ray, and Swarup Bhunia. 2025. LASSO: LLM-Aided Security Property Generation for Assertion- based SoC Verification. In2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD). IEEE, 1–10

    2025

  8. [8]

    Ahmet Cagri Bagbaba, Felipe Augusto da Silva, Matteo Sonza Reorda, Said Ham- dioui, Maksim Jenihhin, and Christian Sauer. 2022. Automated identification of application-dependent safe faults in automotive systems-on-a-chips.Electronics 11, 3 (2022), 319

    2022

  9. [9]

    Yunsheng Bai, Ghaith Bany Hamad, Syed Suhaib, and Haoxing Ren. 2025. Asser- tionForge: Enhancing Formal Verification Assertion Generation with Structured Representation of Specifications and RTL. InProceedings of the IEEE International Conference on LLM-Aided Design (LAD). Stanford, CA

    2025

  10. [10]

    Alessandro Bernardini, Wolfgang Ecker, and Ulf Schlichtmann. 2016. Where formal verification can help in functional safety analysis. In2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 1–8

    2016

  11. [11]

    Cadence Design Systems, Inc. 2025. Jasper Functional Safety Verification App User Guide.User Guide, Product Version 2025.06

    2025

  12. [12]

    Cadence Design Systems, Inc. 2025. Xcelium Safety Fault Simulator User Guide. User Guide, Product Version 2025.06

    2025

  13. [13]

    Hui-Na Chao, Hua-Wei Li, Xiaoyu Song, Tian-Cheng Wang, and Xiao-Wei Li

  14. [14]

    Journal of Computer Science and Technology35, 5 (2020), 1198–1216

    Evaluating and Constraining Hardware Assertions with Absent Scenarios. Journal of Computer Science and Technology35, 5 (2020), 1198–1216

    2020

  15. [15]

    Arjun Chaudhuri, Ching-Yuan Chen, Jonti Talukdar, Siddarth Madala, Ab- hishek Kumar Dubey, and Krishnendu Chakrabarty. 2021. Efficient fault-criticality analysis for AI accelerators using a neural twin. In2021 IEEE International Test Conference (ITC). IEEE, 73–82

    2021

  16. [16]

    Arjun Chaudhuri, Jonti Talukdar, and Krishnendu Chakrabarty. 2022. Machine Learning for Testing Machine-Learning Hardware: A Virtuous Cycle. InProceed- ings of the 41st IEEE/ACM International Conference on Computer-Aided Design. 1–6

    2022

  17. [17]

    Arjun Chaudhuri, Jonti Talukdar, Jinwook Jung, Gi-Joon Nam, and Krishnendu Chakrabarty. 2021. Fault-criticality assessment for AI accelerators using graph convolutional networks. In2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1596–1599

    2021

  18. [18]

    Arjun Chaudhuri, Jonti Talukdar, Fei Su, and Krishnendu Chakrabarty. 2022. Functional criticality analysis of structural faults in AI accelerators.IEEE Trans- actions on Computer-Aided Design of Integrated Circuits and Systems41, 12 (2022), 5657–5670

    2022

  19. [19]

    Yung-Yuan Chen, Chung-Hsien Hsu, and Kuen-Long Leu. 2009. SoC-level risk assessment using FMEA approach in system design with SystemC. In2009 IEEE International Symposium on Industrial Embedded Systems. IEEE, 82–89

    2009

  20. [20]

    Natalia Cherezova, Konstantin Shibin, Maksim Jenihhin, and Artur Jutman. 2023. Understanding fault-tolerance vulnerabilities in advanced SoC FPGAs for critical applications.Microelectronics Reliability146 (2023), 115010

    2023

  21. [21]

    Edmund Clarke, Armin Biere, Richard Raimi, and Yunshan Zhu. 2001. Bounded model checking using satisfiability solving.Formal methods in system design19, 1 (2001), 7–34

    2001

  22. [22]

    Felipe Augusto da Silva, Ahmet Cagri Bagbaba, Said Hamdioui, and Christian Sauer. 2021. An automated formal-based approach for reducing undetected faults in ISO 26262 hardware compliant designs. In2021 IEEE International Test Conference (ITC). IEEE, 329–333

    2021

  23. [23]

    Felipe Augusto da Silva, Ahmet Cagri Bagbaba, Sandro Sartoni, Riccardo Cantoro, Matteo Sonza Reorda, Said Hamdioui, and Christian Sauer. 2020. Determined- Safe Faults Identification: A step towards ISO26262 hardware compliant designs. In2020 IEEE European Test Symposium (ETS). IEEE, 1–6

    2020

  24. [24]

    Alessandro Danese, Nicolò Dalla Riva, and Graziano Pravadelli. 2017. A-team: Automatic template-based assertion miner. InProceedings of the 54th Annual Design Automation Conference 2017. 1–6

    2017

  25. [25]

    Sanjay Das, Shamik Kundu, Pooja Madhusoodhanan, Prasanth Viswanathan Pillai, Rubin Parekhji, Arnab Raha, Suvadeep Banerjee, Suriya Natarajan, and Kanad Basu. 2024. Graph Learning-based Fault Criticality Analysis for Enhancing Functional Safety of E/E Systems. InProceedings of the 61st ACM/IEEE Design Automation Conference. 1–6

    2024

  26. [26]

    Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2024. From local to global: A graph rag approach to query-focused summarization.arXiv preprint arXiv:2404.16130(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  27. [27]

    Shift-Left DFT

    Hiroyuki Iwata, Yoichi Maeda, Jun Matsushima, Oussama Laouamri, Naveen Khanna, Jeff Mayer, and Nilanjan Mukherjee. 2023. A New Framework for RTL Test Points Insertion Facilitating a “Shift-Left DFT” Strategy. In2023 IEEE International Test Conference (ITC). IEEE, 1–10

    2023

  28. [28]

    JEDEC Solid State Technology Association. 2013. Dictionary of Terms for Solid- State Technology — 6th Edition

    2013

  29. [29]

    Rahul Kande, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Shailja Thakur, Ramesh Karri, and Jeyavijayan Rajendran. 2024. (Security) assertions by large language models.IEEE Transactions on Information Forensics and Security 19 (2024), 4374–4389

    2024

  30. [30]

    Andraž Kontarček, Primož Bajec, Mitja Nemec, Vanja Ambrožič, and David Nedeljković. 2015. Cost-effective three-phase PMSM drive tolerant to open-phase fault.IEEE Transactions on Industrial Electronics62, 11 (2015), 6708–6718

    2015

  31. [31]

    Haoran Luo, Guanting Chen, Yandan Zheng, Xiaobao Wu, Yikai Guo, Qika Lin, Yu Feng, Zemin Kuang, Meina Song, Yifan Zhu, et al. 2025. HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Repre- sentation.arXiv preprint arXiv:2503.21322(2025)

    work page arXiv 2025

  32. [32]

    MS Merzoug, F Naceri, et al . 2008. Comparison of field-oriented control and direct torque control for permanent magnet synchronous motor (PMSM).World Academy of Science, Engineering and Technology45 (2008), 299–304

    2008

  33. [33]

    Alessandra Nardi and Antonino Armato. 2017. Functional safety methodolo- gies for automotive applications. In2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 970–975

    2017

  34. [34]

    Marcelo Orenes-Vera, Aninda Manocha, David Wentzlaff, and Margaret Martonosi. 2021. Autosva: Democratizing formal verification of rtl module interactions. In2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 535–540

    2021

  35. [35]

    Rob Palin, David Ward, Ibrahim Habli, and Roger Rivett. 2011. ISO 26262 safety cases: Compliance and assurance. In6th IET International Conference on System Safety 2011. IET, B12

    2011

  36. [36]

    Subhajit Paul, Ansuman Banerjee, Sumana Ghosh, Sudhakar Surendran, and Raj Kumar Gajavelly. 2025. LISA: LLM Informed Systemverilog Assertion gener- ation with RAG and Chain-of-Thought. In2025 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Vol. 1. IEEE, 1–6

    2025

  37. [37]

    Mahesh Prabhu and Jacob A Abraham. 2012. Functional test generation for hard to detect stuck-at faults using RTL model checking. In2012 17th IEEE European Test Symposium (ETS). IEEE, 1–6

    2012

  38. [38]

    Vaishnavi Pulavarthi, Deeksha Nandal, Soham Dan, and Debjit Pal. 2025. Are LLMs Ready for Practical Adoption for Assertion Generation?. In2025 Design, Automation & Test in Europe Conference (DATE). IEEE, 1–7

    2025

  39. [39]

    Vaishnavi Pulavarthi, Deeksha Nandal, Soham Dan, and Debjit Pal. 2025. As- sertionbench: A benchmark to evaluate large-language models for assertion generation. InFindings of the Association for Computational Linguistics: NAACL

    2025

  40. [40]

    Syed Qutub, Florian Geissler, Yang Peng, Ralf Gräfe, Michael Paulitsch, Gereon Hinz, and Alois Knoll. 2022. Hardware faults that matter: understanding and estimating the safety impact of hardware faults on object detection DNNs. In International Conference on Computer Safety, Reliability, and Security. Springer, 298–318

    2022

  41. [41]

    Shinya Takamaeda-Yamazaki. 2015. Pyverilog: A python-based hardware de- sign processing toolkit for verilog hdl. InInternational Symposium on Applied Reconfigurable Computing. Springer, 451–460

    2015

  42. [42]

    Enyuan Tian, Yiwei Ci, Qiusong Yang, Yufeng Li, and Zhichao Lyu. 2025. Assert- Coder: LLM-Based Assertion Generation via Multimodal Specification Extraction. arXiv preprint arXiv:2507.10338(2025)

    work page Pith review arXiv 2025

  43. [43]

    Adrian Traskov, Thorsten Ehrenberg, Sacha Loitz, Abdelouahab Ayari, Avidan Efody, and Joseph Hupcey III. 2016. Fault proof: Using formal techniques for safety verification and fault analysis. In2016 Design and Verification Conference and Exhibition DVCON Europe. DVCON. 27–32

    2016

  44. [44]

    Xuan Wang. 2025. FPGA-FOC: An FPGA-based Field Oriented Control (FOC) for driving BLDC/PMSM motor. https://github.com/WangXuan95/FPGA-FOC

    2025

  45. [45]

    Zhiyuan Yan, Wenji Fang, Mengming Li, Min Li, Shang Liu, Zhiyao Xie, and Hongce Zhang. 2025. Assertllm: Generating hardware verification assertions from design specifications via multi-llms. InProceedings of the 30th Asia and South Pacific Design Automation Conference. 614–621

    2025

  46. [46]

    Ping Yeung, Doug Smith, and Abdelouahab Ayari. 2018. Whose fault is it formally? formal techniques for optimizing iso 26262 fault analysis. (2018)

    2018