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REVIEW 2 major objections 67 references

A geometric framework turns sparse yarn data into stitch-resolved descriptors that show how knit deformation is shared among reorientation, loop bending, surface bending, and dilation.

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-4.5

2026-07-15 11:27 UTC pith:6KHYGFBY

load-bearing objection The abstract for the knit-geometry paper is coherent, but the supplied full text is a different paper (SAGE, LLM vulnerability detection), so the central claims cannot be audited. the 2 major comments →

arxiv 2604.19030 v2 pith:6KHYGFBY submitted 2026-04-21 cond-mat.soft cs.NAmath.NA

Geometric quantification for nonlinear deformation in knitted fabrics

Jiani Fang , Xiaoxiao Ding , Gary P. T. Choi This is my paper
classification cond-mat.soft cs.NAmath.NA
keywords knitted fabricsgeometric nonlinearityyarn centerline reconstructionstitch-resolved deformationarchitected materialssoft matter mechanicsgeometric state spaceshape morphing
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.

Knitted fabrics can undergo large, recoverable shape change because their stitches rearrange rather than the yarn material itself failing. Designers have used that geometric nonlinearity for a long time, but lacked a quantitative, stitch-by-stitch account of how deformation is partitioned and how it evolves. This paper supplies a reconstruction pipeline that turns sparse yarn-level data into smooth yarn centerlines and fabric surfaces, then extracts readable geometric descriptors from them. Applied to representative knits, those descriptors separate global stretch into stitch reorientation, loop bending, surface bending, and dilation, and they track where large geometric change appears, stays, and moves over time. The resulting geometric state space is offered as a common language for comparing structures, flagging candidate sites of mechanical localization, and linking geometry to constitutive models and inverse design.

Core claim

From sparse yarn-level representations one can reconstruct smooth yarn centerlines and fabric surfaces and extract multi-dimensional geometric descriptors that resolve how a knit’s overall deformation is distributed among stitch reorientation, loop bending, surface bending, and dilation, while also mapping how regions of large geometric variation emerge, persist, and redistribute over time—thereby defining a unified geometric state space for structure comparison and candidate mechanical-localization regions without directly measuring stress.

What carries the argument

The geometric quantification framework: sparse-to-smooth reconstruction of yarn centerlines and fabric surfaces, followed by extraction of interpretable multi-dimensional descriptors that partition deformation modes and track their temporal redistribution.

Load-bearing premise

That purely geometric descriptors of reconstructed yarns and surfaces, without measuring stress or force, are enough to mark where mechanics will concentrate and to serve as the intermediate representation for constitutive models and inverse design.

What would settle it

On a loaded knit with simultaneous geometry tracking and local stress or damage measurement, check whether the regions the descriptors flag as large geometric variation actually coincide with measured stress peaks or failure sites; systematic mismatch would collapse the claim that geometry alone identifies mechanical localization.

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

If this is right

  • Different knit architectures can be compared in one geometric state space by how they partition the same global deformation among the four modes.
  • Time series of the descriptors can show which high-variation regions persist versus migrate, guiding where reinforcement or sensing should be placed.
  • Constitutive and damage models can be driven by these geometric coordinates instead of raw mesh kinematics alone.
  • Graph-based inverse design can target desired geometric-state trajectories rather than only final shapes.
  • Experimental imaging that yields sparse yarn positions can be converted into the same descriptors for direct comparison with simulation.

Where Pith is reading between the lines

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

  • If the four-mode partition is stable across yarns and gauges, it could become a compact feature set for classifying knit topologies by deformation strategy.
  • The same reconstruction-plus-descriptor pipeline may transfer to other looped or interlocked textiles (e.g., crochet, weft-knitted composites) where sparse centerline data are available.
  • Coupling the geometric state space to a lightweight graph neural model would let inverse design optimize stitch topology for prescribed localization patterns without full continuum simulation at every step.

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

2 major / 0 minor

Summary. The submission is presented as arXiv:2604.19030, a soft-matter paper proposing a geometric quantification framework that reconstructs yarn centerlines and fabric surfaces from sparse yarn-level data and extracts multi-dimensional descriptors (stitch reorientation, loop bending, surface bending, dilation) to track temporal redistribution of large geometric variation and to define a geometry-based state space for comparing knits and flagging candidate mechanical-localization regions without direct stress measurement. The supplied full manuscript body, however, is an entirely different paper (SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection; arXiv:2604.19031 / ISSTA-style CS security work). No methods, reconstructions, descriptors, figures, or results on knits appear in the body.

Significance. If the abstract’s claims were supported by a matching manuscript—with validated reconstruction fidelity, interpretable descriptors, temporal redistribution evidence, and a demonstrated link from pure geometry to localization candidates usable by constitutive models or inverse design—the work would be a useful contribution to architected soft materials and knit mechanics. As submitted, that significance cannot be assessed: the body does not contain the claimed framework, so no credit can be given for machine-checked proofs, reproducible knit pipelines, parameter-free geometric derivations, or falsifiable localization predictions.

major comments (2)
  1. Complete manuscript mismatch: title, abstract, and paper_id (2604.19030, geometric quantification of knitted fabrics) do not correspond to the full text, which is SAGE (LLM vulnerability detection, arXiv 2604.19031). There is no reconstruction pipeline, yarn-centerline/surface method, stitch-level descriptors, temporal analysis, or localization result for knits. The abstract’s load-bearing claim—that purely geometric descriptors define a unified state space sufficient to identify candidate mechanical-localization regions and couple to constitutive models/inverse design—cannot be audited for internal consistency, validation against stress/force, or temporal evidence. This is not a local fix; the body is the wrong paper.
  2. Even on the abstract alone, the weakest load-bearing assumption (geometry without stress/force suffices for mechanical localization and as an intermediate for constitutive/inverse-design workflows) is asserted without any supporting section, equation, table, or validation protocol in the provided document. No audit of reconstruction fidelity, descriptor interpretability, or redistribution dynamics is possible.

Circularity Check

0 steps flagged

No circular derivation can be audited: supplied full text is the SAGE LLM-vulnerability paper, not the knit-geometry manuscript whose abstract is under review.

full rationale

The review target is arXiv 2604.19030 (geometric quantification of nonlinear deformation in knitted fabrics). The CACHEABLE PAPER SOURCE CONTEXT instead contains the full manuscript of SAGE (Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection; arXiv 2604.19031 / ISSTA-style CS security paper). That body develops task-conditional sparse autoencoders, SNR amplification, and MCC gains on BigVul/PrimeVul/PreciseBugs; it contains no yarn-centerline reconstruction, fabric-surface descriptors, stitch reorientation, loop/surface bending, dilation, temporal redistribution of geometric variation, or geometric state space for mechanical localization. Because none of the abstract’s claimed derivation steps appear in the supplied text, no equation, fit, self-citation chain, uniqueness theorem, or ansatz can be shown to reduce a prediction to its inputs by construction. Under the hard rules, circularity is only reportable when a specific reduction can be quoted; here the derivation chain is simply absent. Score 0 with empty steps is therefore the only honest outcome. (The abstract’s geometry-without-stress sufficiency claim remains a potential definitional risk if the correct manuscript is later supplied, but that risk is not circularity under the present evidence.)

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 1 invented entities

Abstract-only review of a methods paper. Load-bearing content is mostly domain modeling choices rather than free constants or new physical entities. The central claim rests on the premise that sparse yarn-level data can be lifted to smooth centerlines/surfaces whose geometric descriptors meaningfully decompose nonlinear knit deformation and proxy mechanical localization without direct stress measurement.

axioms (3)
  • domain assumption Sparse yarn-level representations of knits can be reconstructed into smooth yarn centerlines and continuous fabric surfaces that preserve the deformation modes of interest.
    Invoked by the abstract’s reconstruction step; without this, the multi-dimensional descriptors are not well-defined on real data.
  • ad hoc to paper Global nonlinear knit deformation can be usefully partitioned into stitch reorientation, loop bending, surface bending, and dilation.
    This is the paper’s chosen descriptor taxonomy; it is presented as an interpretable decomposition rather than a derived uniqueness theorem.
  • domain assumption Geometric variation alone can identify candidate regions of mechanical localization even though stress is not measured.
    Explicit abstract claim that geometric state space substitutes for direct stress measurement when screening localization candidates.
invented entities (1)
  • Geometric quantification framework / multi-dimensional knit deformation descriptors no independent evidence
    purpose: Provide a unified geometric state space for comparing knitted structures and tracking temporal redistribution of large geometric variation.
    The abstract introduces this framework as the main contribution; independent evidence outside the paper is not available in the review package.

pith-pipeline@v1.1.0-grok45 · 17366 in / 2685 out tokens · 28866 ms · 2026-07-15T11:27:13.378819+00:00 · methodology

0 comments
read the original abstract

Knitted fabrics exemplify a broad class of architected materials capable of large deformations, enabling shape morphing, mechanical biocompatibility, and embedded multifunctionality without material damage. Although geometric nonlinearity has been intuitively utilized in their design, a quantitative description of stitch-resolved deformation and its temporal evolution remains lacking. Here, we introduce a geometric quantification framework that reconstructs smooth yarn centerlines and fabric surfaces from sparse yarn-level representations and extracts interpretable descriptors across dimensions. Applied to representative knitted structures, this framework resolves how global deformation is distributed among stitch reorientation, loop bending, surface bending, and dilation. Moreover, it reveals how regions of large geometric variation emerge, persist, and redistribute over time. Rather than directly measuring stress, these geometric descriptors define a unified geometric state space for comparing knitted structures and identifying candidate regions of mechanical localization. The framework provides a quantitative language for nonlinear deformation in knits and establishes a geometry-based representation that can be coupled to constitutive models, experimental measurements, and graph-based inverse-design workflows.

Figures

Figures reproduced from arXiv: 2604.19030 by Gary P. T. Choi, Jiani Fang, Xiaoxiao Ding.

Figure 1
Figure 1. Figure 1: An illustration of our geometric quantification framework. (a) Conceptual workflow motivating the quantification of localized mechanical responses in hybrid fabrics, such as guiding the inverse design of an optimally conformable patch for cyclists during training sessions. A knitted sleeve undergoes temporal evolution under large deformation while exhibiting spatial variation represented by local constitut… view at source ↗
Figure 2
Figure 2. Figure 2: Quantification of the anisotropic mechanical responses in the jersey pattern. Spatial distribution of temporal changes in five representative quantities under tensile strain from 0% to 120%: (b) Curve curvature. (c) Curve torsion. (d) Gaussian curvature. (e) Area. (f) Volume. Loading directions: [I] Tension along the weft direction. [II] Tension along the warp direction. information and line graphs showing… view at source ↗
Figure 3
Figure 3. Figure 3: Heterogeneous mechanical responses and hot spots of Gaussian curvature under warp-direction tension. (a)–(d) Gaussian curvature change in jersey fabric with 20%, 60%, 80%, and 120% tensile strain in warp direction. (e) Time-aggregated maps of changes in Gaussian curvature for jersey fabric, generated by overlaying normalized heat maps acquired during successive tensile loading at 0%, 20%, 40%, 60%, 80%, 10… view at source ↗
Figure 4
Figure 4. Figure 4: Quantifying the mechanical responses from mixed-pattern fabrics. Spatial distribution of geometric quantity changes in two mixed-pattern fabrics under uniaxial tensile strain from 0% to 180% applied along the warp direction. (a) Visualization for pattern A. (b) Visualization for pattern B. (c) Curve curvature change for pattern A. (d) Curve curvature change for pattern B. (e) Aspect ratio change for patter… view at source ↗
Figure 5
Figure 5. Figure 5: Temporal changes in spatial variations of four representative quantities for jersey fabric, recorded across a tensile strain range of 0% to 120%. (a) Curve curvature, κ. (b) Curve torsion, τ . (c) Gaussian curvature, K. (d) Area. (e) Volume. Loading directions: [I] Weft. [II] Warp. In each of (a)–(e), the left plot corresponds to variation along the weft direction and the right plot corresponds to variatio… view at source ↗
Figure 6
Figure 6. Figure 6: Quantification methods applied on the deformed cylinder sample. (a) The deformed cylinder sample. (b)–(h) Spatial distribution of change in seven representative quantities as the bending angle increases from 0◦ to 60◦ . (b) Curve curvature. (c) Curve torsion. (d) Area. (e) Aspect ratio. (f) Gaussian curvature change. (g) Mean curvature. (h) Volume. (i)–(o) Temporal changes of spatial variation in seven rep… view at source ↗

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Works this paper leans on

67 extracted references · 14 linked inside Pith

  1. [1]

    Towards Monosemanticity: Decomposing Language Models With Dictionary Learning

    2023. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning. https://www.anthropic. com/news/towards-monosemanticity-decomposing-language-models-with-dictionary-learning [Online; accessed 2025-12-08]

    2023

  2. [2]

    ChatGPT-5.2-Codex

    2025. ChatGPT-5.2-Codex. https://openai.com/index/introducing-gpt-5-2-codex/ [Online; accessed 2025-12-23]

    2025

  3. [3]

    Claude-Opus-4.5

    2025. Claude-Opus-4.5. https://www.anthropic.com/news/claude-opus-4-5 [Online; accessed 2025-12-23]

    2025

  4. [4]

    Gemini-3-pro

    2025. Gemini-3-pro. https://deepmind.google/models/gemini/pro/ [Online; accessed 2025-12-23]

    2025

  5. [5]

    2025. NIST. https://nvd.nist.gov/general/nvd-dashboard [Online; accessed 2025-12-08]

    2025

  6. [6]

    Al Bessey, Ken Block, Ben Chelf, Andy Chou, Bryan Fulton, Seth Hallem, Charles Henri-Gros, Asya Kamsky, Scott McPeak, and Dawson Engler. 2010. A few billion lines of code later: using static analysis to find bugs in the real world. Commun. ACM53, 2 (Feb. 2010), 66–75. doi:10.1145/1646353.1646374 , Vol. 1, No. 1, Article . Publication date: April 2018. SAG...

    doi:10.1145/1646353.1646374 2010

  7. [7]

    Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M. Buhmann. 2010. The Balanced Accuracy and Its Posterior Distribution. In2010 20th International Conference on Pattern Recognition. 3121–3124. doi:10.1109/ICPR.2010.764

    doi:10.1109/icpr.2010.764 2010

  8. [8]

    Partha Chakraborty, Krishna Kanth Arumugam, Mahmoud Alfadel, Meiyappan Nagappan, and Shane McIntosh. 2024. Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets.IEEE Trans. Software Eng.50, 8 (2024), 2163–2177. doi:10.1109/TSE.2024.3423712

    doi:10.1109/tse.2024.3423712 2024

  9. [9]

    Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2022. Deep Learning Based Vulnerability Detection: Are We There Yet?IEEE Transactions on Software Engineering48, 9 (2022), 3280–3296. doi:10.1109/TSE.2021. 3087402

    doi:10.1109/tse.2021 2022

  10. [10]

    Davide Chicco and Giuseppe Jurman. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC Genomics21, 1 (02 Jan 2020), 6. doi:10.1186/s12864-019-6413-7

    doi:10.1186/s12864-019-6413-7 2020

  11. [11]

    Ali Babar, and M

    Roland Croft, M. Ali Babar, and M. Mehdi Kholoosi. 2023. Data Quality for Software Vulnerability Datasets. In Proceedings of the 45th International Conference on Software Engineering(Melbourne, Victoria, Australia)(ICSE ’23). IEEE Press, 121–133. doi:10.1109/ICSE48619.2023.00022

    doi:10.1109/icse48619.2023.00022 2023

  12. [12]

    Lei Cui, Jiancong Cui, Zhiyu Hao, Lun Li, Zhenquan Ding, and Yongji Liu. 2022. An empirical study of vulnerability discovery methods over the past ten years.Comput. Secur.120 (2022), 102817. doi:10.1016/J.COSE.2022.102817

    doi:10.1016/j.cose.2022.102817 2022

  13. [13]

    DeepSeek-AI. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.CoRR abs/2501.12948 (2025). arXiv:2501.12948 doi:10.48550/ARXIV.2501.12948

    Pith/arXiv arXiv doi:10.48550/arxiv.2501.12948 2025

  14. [14]

    Gelei Deng, Yi Liu, Victor Mayoral Vilches, Peng Liu, Yuekang Li, Yuan Xu, Tianwei Zhang, Yang Liu, Martin Pinzger, and Stefan Rass. 2023. PentestGPT: An LLM-empowered Automatic Penetration Testing Tool.CoRRabs/2308.06782 (2023). arXiv:2308.06782 doi:10.48550/ARXIV.2308.06782

    Pith/arXiv arXiv doi:10.48550/arxiv.2308.06782 2023

  15. [15]

    Wagner, Baishakhi Ray, and Yizheng Chen

    Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David A. Wagner, Baishakhi Ray, and Yizheng Chen. 2025. Vulnerability Detection with Code Language Models: How Far are We?. In 47th IEEE/ACM International Conference on Software Engineering, ICSE 2025, Ottawa, ON, Canada, April 26 - May 6,

    2025

  16. [16]

    doi:10.1109/ICSE55347.2025.00038

    IEEE, 1729–1741. doi:10.1109/ICSE55347.2025.00038

    doi:10.1109/icse55347.2025.00038 2025

  17. [17]

    Xiaohu Du, Ming Wen, Jiahao Zhu, Zifan Xie, Bin Ji, Huijun Liu, Xuanhua Shi, and Hai Jin. 2024. Generalization- Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning. InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Compu- tational Linguistic...

    doi:10.18653/v1/2024.findings-acl.625 2024

  18. [18]

    Jiahao Fan, Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries. InProceedings of the 17th International Conference on Mining Software Repositories(Seoul, Republic of Korea)(MSR ’20). Association for Computing Machinery, New York, NY, USA, 508–512. doi:10.1145/3379597.3387501

    doi:10.1145/3379597.3387501 2020

  19. [19]

    Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 (Findings of ACL, Vol. EMNLP 2020), Trev...

    doi:10.18653/v1/2020.findings-emnlp.139 2020

  20. [20]

    Michael Fu and Chakkrit Tantithamthavorn. 2022. LineVul: a transformer-based line-level vulnerability prediction. In Proceedings of the 19th International Conference on Mining Software Repositories(Pittsburgh, Pennsylvania)(MSR ’22). Association for Computing Machinery, New York, NY, USA, 608–620. doi:10.1145/3524842.3528452

    doi:10.1145/3524842.3528452 2022

  21. [21]

    Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022. UniXcoder: Unified Cross-Modal Pre-training for Code Representation. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Smaranda Muresan, Preslav Nakov, and Aline Villavicenci...

    doi:10.18653/v1/2022.acl-long.499 2022

  22. [22]

    Jingxuan He and Martin Vechev. 2023. Large Language Models for Code: Security Hardening and Adversarial Testing. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23). ACM, 1865–1879. doi:10.1145/3576915.3623175

    doi:10.1145/3576915.3623175 2023

  23. [23]

    Y. He, Z. Chen, and C. Le Goues. 2023. PreciseBugCollector: Extensible, Executable and Precise Bug-Fix Collection: Solution for Challenge 8: Automating Precise Data Collection for Code Snippets with Bugs, Fixes, Locations, and Types. In2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE Computer Society, Los Alamitos,...

    doi:10.1109/ase56229.2023.00163 2023

  24. [24]

    Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, An Yang, Rui Men, Fei Huang, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, and Junyang Lin. 2024. Qwen2.5-Coder Technical Report.CoRRabs/2409.12186 (2024). arXiv:2409.12186 doi:10.48550/ARXIV.2409.12186

    Pith/arXiv arXiv doi:10.48550/arxiv.2409.12186 2024

  25. [25]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. Mistral 7B. , Vol. 1, No. 1,...

    Pith/arXiv arXiv doi:10.48550/arxiv.2310.06825 2023

  26. [26]

    Jiayuan Li, Lei Cui, Jie Zhang, Haiqiang Fei, Yu Chen, and Hongsong Zhu. 2025. Steering Large Language Models for Vulnerability Detection. InICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1–5. doi:10.1109/ICASSP49660.2025.10887736

    doi:10.1109/icassp49660.2025.10887736 2025

  27. [27]

    Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021. Vulnerability detection with fine-grained interpretations. InProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’21). ACM, 292–303. doi:10.1145/3468264.3468597

    doi:10.1145/3468264.3468597 2021

  28. [28]

    Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, and Zhaoxuan Chen. 2022. SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities.IEEE Transactions on Dependable and Secure Computing19, 4 (2022), 2244–2258. doi:10.1109/TDSC.2021.3051525

    doi:10.1109/tdsc.2021.3051525 2022

  29. [29]

    Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018. VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. InProceedings 2018 Network and Distributed System Security Symposium (NDSS 2018). Internet Society. doi:10.14722/ndss.2018.23158

    doi:10.14722/ndss.2018.23158 2018

  30. [30]

    Dragan, Rohin Shah, and Neel Nanda

    Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca D. Dragan, Rohin Shah, and Neel Nanda. 2024. Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2.CoRRabs/2408.05147 (2024). arXiv:2408.05147 doi:10.48550/ARXIV.2408.05147

    Pith/arXiv arXiv doi:10.48550/arxiv.2408.05147 2024

  31. [31]

    Zhongxin Liu, Zhijie Tang, Junwei Zhang, Xin Xia, and Xiaohu Yang. 2024. Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks. InProceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024. ACM, 151:1–151:13. doi:10.1145/3597503.3639142

    doi:10.1145/3597503.3639142 2024

  32. [32]

    Benjamin Livshits and Monica S. Lam. 2005. Finding Security Vulnerabilities in Java Applications with Static Analysis. InUSENIX Security Symposium. https://api.semanticscholar.org/CorpusID:8766314

    2005

  33. [33]

    Nelson Amaral, Bor-Yuh Evan Chang, Samuel Z

    Benjamin Livshits, Manu Sridharan, Yannis Smaragdakis, Ondřej Lhoták, J. Nelson Amaral, Bor-Yuh Evan Chang, Samuel Z. Guyer, Uday P. Khedker, Anders Møller, and Dimitrios Vardoulakis. 2015. In defense of soundiness: a manifesto.Commun. ACM58, 2 (Jan. 2015), 44–46. doi:10.1145/2644805

    doi:10.1145/2644805 2015

  34. [34]

    Benjamin Livshits and Monica S

    V. Benjamin Livshits and Monica S. Lam. 2005. Finding Security Vulnerabilities in Java Applications with Static Analysis. InProceedings of the 14th USENIX Security Symposium, Baltimore, MD, USA, July 31 - August 5, 2005, Patrick D. McDaniel (Ed.). USENIX Association. https://www.usenix.org/conference/14th-usenix-security-symposium/finding- security-vulner...

    2005

  35. [35]

    Changhua Luo, Wei Meng, and Shuai Wang. 2024. Strengthening Supply Chain Security with Fine-grained Safe Patch Identification. InProceedings of the IEEE/ACM 46th International Conference on Software Engineering(Lisbon, Portugal) (ICSE ’24). Association for Computing Machinery, New York, NY, USA, Article 89, 12 pages. doi:10.1145/3597503.3639104

    doi:10.1145/3597503.3639104 2024

  36. [36]

    Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, and Yang Liu. 2024. Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities.ACM Trans. Softw. Eng. Methodol.33, 7, Article 169 (Aug. 2024), 29 pages. doi:10.1145/3664606

    doi:10.1145/3664606 2024

  37. [37]

    Manès, HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J

    Valentin J.M. Manès, HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J. Schwartz, and Maverick Woo. 2021. The Art, Science, and Engineering of Fuzzing: A Survey.IEEE Transactions on Software Engineering47, 11 (2021), 2312–2331. doi:10.1109/TSE.2019.2946563

    doi:10.1109/tse.2019.2946563 2021

  38. [38]

    Zhenyu Mao, Jialong Li, Dongming Jin, Munan Li, and Kenji Tei. 2024. Multi-Role Consensus Through LLMs Discussions for Vulnerability Detection. In24th IEEE International Conference on Software Quality, Reliability, and Security, QRS - Companion, Cambridge, United Kingdom, July 1-5, 2024. IEEE, 1318–1319. doi:10.1109/QRS-C63300.2024.00173

    doi:10.1109/qrs-c63300.2024.00173 2024

  39. [39]

    Matthews

    B.W. Matthews. 1975. Comparison of the predicted and observed secondary structure of T4 phage lysozyme.Biochimica et Biophysica Acta (BBA) - Protein Structure405, 2 (1975), 442–451. doi:10.1016/0005-2795(75)90109-9

    doi:10.1016/0005-2795(75)90109-9 1975

  40. [40]

    Rui Melo, Cláudia Mamede, Andre Catarino, Rui Abreu, and Henrique Lopes Cardoso. 2025. Are Sparse Autoencoders Useful for Java Function Bug Detection?CoRRabs/2505.10375 (2025). arXiv:2505.10375 doi:10.48550/ARXIV.2505.10375

    Pith/arXiv arXiv doi:10.48550/arxiv.2505.10375 2025

  41. [41]

    Kiho Park, Yo Joong Choe, and Victor Veitch. 2024. The Linear Representation Hypothesis and the Geometry of Large Language Models. InForty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27,

    2024

  42. [42]

    https://openreview.net/forum?id=UGpGkLzwpP

    OpenReview.net. https://openreview.net/forum?id=UGpGkLzwpP

  43. [43]

    Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, and Neel Nanda. 2024. Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders.CoRR abs/2407.14435 (2024). arXiv:2407.14435 doi:10.48550/ARXIV.2407.14435

    Pith/arXiv arXiv doi:10.48550/arxiv.2407.14435 2024

  44. [44]

    Zilong Ren, Xiaolin Ju, Xiang Chen, and Hao Shen. 2024. ProRLearn: boosting prompt tuning-based vulnerability detection by reinforcement learning.Automated Software Engineering31, 2 (2024), 38. doi:10.1007/s10515-024-00438-9

    doi:10.1007/s10515-024-00438-9 2024

  45. [45]

    Niklas Risse and Marcel Böhme. 2024. Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection. In33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024, Davide Balzarotti and Wenyuan Xu (Eds.). USENIX Association. https://www.usenix.org/conference/usenixsecurity24/presentation/risse

    2024

  46. [46]

    Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton-Ferrer, Aaron , Vol. 1, No. 1, Article . Publication date: April 2018. SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerabili...

    Pith/arXiv arXiv doi:10.48550/arxiv.2308.12950 2018

  47. [47]

    Hossain Shahriar and Mohammad Zulkernine. 2012. Mitigating program security vulnerabilities: Approaches and challenges.ACM Comput. Surv.44, 3, Article 11 (June 2012), 46 pages. doi:10.1145/2187671.2187673

    doi:10.1145/2187671.2187673 2012

  48. [48]

    Qingkai Shi, Xiao Xiao, Rongxin Wu, Jinguo Zhou, Gang Fan, and Charles Zhang. 2018. Pinpoint: fast and precise sparse value flow analysis for million lines of code.SIGPLAN Not.53, 4 (June 2018), 693–706. doi:10.1145/3296979.3192418

    doi:10.1145/3296979.3192418 2018

  49. [49]

    Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K. Reddy. 2023. Execution-based Code Generation using Deep Reinforcement Learning.Trans. Mach. Learn. Res.2023 (2023). https://openreview.net/forum?id=0XBuaxqEcG

    2023

  50. [50]

    Huizhen Shu, Xuying Li, and Zhuo Li. 2025. LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation.CoRRabs/2509.19839 (2025). arXiv:2509.19839 doi:10.48550/ARXIV.2509.19839

    doi:10.48550/arxiv.2509.19839 2025

  51. [51]

    Marco Simoni, Aleksandar Fontana, Giulio Rossolini, and Andrea Saracino. 2025. Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimization.CoRRabs/2507.03051 (2025). arXiv:2507.03051 doi:10.48550/ARXIV.2507.03051

    Pith/arXiv arXiv doi:10.48550/arxiv.2507.03051 2025

  52. [52]

    Barr, and Wei Le

    Benjamin Steenhoek, Md Mahbubur Rahman, Monoshi Kumar Roy, Mirza Sanjida Alam, Earl T. Barr, and Wei Le

  53. [53]

    arXiv:2403.17218 doi:10.48550/ARXIV.2403.17218

    A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection.CoRR abs/2403.17218 (2024). arXiv:2403.17218 doi:10.48550/ARXIV.2403.17218

    Pith/arXiv arXiv doi:10.48550/arxiv.2403.17218 2024

  54. [54]

    Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Wei Ma, Lyuye Zhang, Miaolei Shi, and Yang Liu. 2024. LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs’ Vulnerability Reasoning.CoRRabs/2401.16185 (2024). arXiv:2401.16185 doi:10.48550/ARXIV.2401.16185

    Pith/arXiv arXiv doi:10.48550/arxiv.2401.16185 2024

  55. [55]

    Llama Team. 2024. The Llama 3 Herd of Models.CoRRabs/2407.21783 (2024). arXiv:2407.21783 doi:10.48550/ARXIV. 2407.21783

    Pith/arXiv arXiv doi:10.48550/arxiv 2024

  56. [56]

    Chung-Nan Tsai, Xin Wang, Cheng-Hsiung Lee, and Ching-Sheng Lin. 2025. A Sequential Multi-Stage Approach for Code Vulnerability Detection via Confidence- and Collaboration-based Decision Making. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Viole...

    2025

  57. [57]

    Viega, J.T

    J. Viega, J.T. Bloch, Y. Kohno, and G. McGraw. 2000. ITS4: a static vulnerability scanner for C and C++ code. InProceedings 16th Annual Computer Security Applications Conference (ACSAC’00). 257–267. doi:10.1109/ACSAC.2000.898880

    doi:10.1109/acsac.2000.898880 2000

  58. [58]

    Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin. 2022. What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code. In44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022. ACM, 2377–2388. doi:10.1145/3510003.3510050

    doi:10.1145/3510003.3510050 2022

  59. [59]

    Xin-Cheng Wen, Yijun Yang, Cuiyun Gao, Yang Xiao, and Deheng Ye. 2025. Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data. InFindings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025 (Findings of ACL, Vol. ACL 2025), Wanxiang Che, Joyce Nabende,...

    2025

  60. [60]

    Xin-Cheng Wen, Xinchen Wang, Cuiyun Gao, Shaohua Wang, Yang Liu, and Zhaoquan Gu. 2023. When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection. In2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 345–357. doi:10.1109/ASE56229.2023.00144

    doi:10.1109/ase56229.2023.00144 2023

  61. [61]

    Fangzhou Wu, Qingzhao Zhang, Ati Priya Bajaj, Tiffany Bao, Ning Zhang, Ruoyu Wang, and Chaowei Xiao. 2023. Exploring the Limits of ChatGPT in Software Security Applications.CoRRabs/2312.05275 (2023). arXiv:2312.05275 doi:10.48550/ARXIV.2312.05275

    Pith/arXiv arXiv doi:10.48550/arxiv.2312.05275 2023

  62. [62]

    Fabian Yamaguchi, Nico Golde, Daniel Arp, and Konrad Rieck. 2014. Modeling and Discovering Vulnerabilities with Code Property Graphs. In2014 IEEE Symposium on Security and Privacy. 590–604. doi:10.1109/SP.2014.44

    doi:10.1109/sp.2014.44 2014

  63. [63]

    Aidan Z. H. Yang, Haoye Tian, He Ye, Ruben Martins, and Claire Le Goues. 2024. Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models.CoRRabs/2406.05892 (2024). arXiv:2406.05892 doi:10.48550/ARXIV.2406.05892

    Pith/arXiv arXiv doi:10.48550/arxiv.2406.05892 2024

  64. [64]

    Chenyuan Zhang, Hao Liu, Jiutian Zeng, Kejing Yang, Yuhong Li, and Hui Li. 2024. Prompt-Enhanced Software Vulnerability Detection Using ChatGPT. InProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, ICSE Companion 2024, Lisbon, Portugal, April 14-20, 2024. ACM, 276–277. doi:10. 1145/3639478.3643065

    arXiv 2024

  65. [65]

    Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, and Yang Liu. 2023. A Black-Box Attack on Code Models via Representation Nearest Neighbor Search. InFindings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 (Findings of ACL, Vol. EMNLP 2023), Houda Bouamor, Juan Pino, and Kalika Bali (Eds....

    doi:10.18653/v1/2023.findings-emnlp.649 2023

  66. [66]

    Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024. AutoCodeRover: Autonomous Program Improvement. InProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2024, Vienna, Austria, September 16-20, 2024, Maria Christakis and Michael Pradel (Eds.). ACM, 1592–1604. doi:10.1145/3650212.3680384

    doi:10.1145/3650212.3680384 2024

  67. [67]

    Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective Vulnerability Iden- tification by Learning Comprehensive Program Semantics via Graph Neural Networks. InAdvances in Neural In- formation Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vanc...

    2019