Pith. sign in

REVIEW 4 major objections 2 minor 42 references

This paper claims that periodically vibrating a needle produces a measurable energy signal that lets a robotic ultrasound system recover alignment between the imaging plane and the needle plane even when the needle is completely invisible i

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 ↗

A vibration-based energy metric, obtained by periodically vibrating the needle, is used as an out-of-plane feedback signal to re-align the ultrasound probe with the needle plane, reporting 0.41 mm translation and 0.51 degree rotation errors on ex-vivo tissue.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The abstract promises a useful vibration-based needle alignment signal for robotic ultrasound, but the supplied full text is an unrelated C-to-Rust paper, so nothing is actually assessable. the 4 major comments →

arxiv 2508.06921 v2 pith:JAT2D6MG submitted 2025-08-09 cs.RO cs.CV

Vibration-Based Energy Metric for Restoring Needle Alignment in Autonomous Robotic Ultrasound

Zhongyu Chen , Chenyang Li , Xuesong Li , Dianye Huang , Zhongliang Jiang , Stefanie Speidel , Xiangyu Chu , K. W. Samuel Au This is my paper
classification cs.RO cs.CV
keywords vibration-based energy metricneedle alignmentrobotic ultrasoundout-of-plane needlepercutaneous needle insertionimage-guided interventioncontrol strategyex-vivo tissue
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 reading

The paper is trying to establish a new feedback signal for robotic ultrasound-guided needle insertion: instead of detecting the needle in the image, the robot vibrates the needle mechanically and reads the vibration energy from the ultrasound system. The claim is that this energy metric remains informative even when the needle is fully out of the imaging plane, which is precisely the situation where image-based needle detection fails. Using this metric, the paper develops a control strategy that moves and rotates the ultrasound probe to minimize the energy, restoring alignment. Ex-vivo porcine tissue experiments with a dual-arm robot report a translational error of 0.41 $\pm$ 0.27 mm and a rotational error of 0.51 $\pm$ 0.19 degrees. If correct, the work provides a visibility-free way to recover needle-plane alignment that existing image-based methods cannot.

Core claim

The central discovery claimed is that the vibration energy of a periodically actuated needle, as measured through the ultrasound system, is a usable indicator of misalignment between the ultrasound imaging plane and the needle insertion plane, even when the needle is completely out of plane. The paper proposes a vibration-based energy metric and a control strategy that uses this metric as feedback to adjust the probe's translation and rotation. The authors report experimental results on ex-vivo porcine tissue showing a translational error of 0.41 $\pm$ 0.27 mm and a rotational error of 0.51 $\pm$ 0.19 degrees, which they attribute to the effectiveness of the proposed metric and control strat

What carries the argument

The central mechanism is the vibration-based energy metric. A mechanical system periodically vibrates the needle; the energy of that vibration, as captured by the ultrasound imaging system, serves as a feedback signal. The control strategy iteratively repositions the ultrasound probe—in translation and rotation—to minimize the metric, thereby restoring alignment between the imaging plane and the needle insertion plane. The metric's key property is that it remains effective when the needle is fully out of plane, unlike image-based needle detection.

Load-bearing premise

The whole approach depends on the vibration energy signal being a monotonic, separable function of out-of-plane misalignment that survives tissue attenuation and is not confounded by speckle or tissue motion—a relationship asserted from experiments, not derived from first principles.

What would settle it

Measure the vibration energy metric as the imaging plane is deliberately shifted out of plane by known distances in a tissue-mimicking phantom, and check whether the metric changes monotonically without plateaus. If the signal flattens before full misalignment, or if physiological motion (e.g., breathing, cardiac pulse) creates comparable energy changes, the control strategy cannot reliably restore alignment in vivo.

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

If this is right

  • Alignment recovery works without needle visibility in the image, so the method applies when the needle is fully out of plane.
  • The approach is robust to speckle noise and needle-like artifacts that degrade image-based detectors.
  • The reported errors (0.41 mm translation, 0.51 degrees rotation) indicate sub-millimeter and sub-degree precision on ex-vivo tissue.
  • The method integrates into a dual-arm robotic ultrasound-guided needle insertion system, offering a new feedback modality for autonomous control.
  • Because the metric is not image-based, it may provide a fallback when ultrasound image quality is poor or the needle is not visible.

Where Pith is reading between the lines

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

  • If the metric is genuinely monotonic with out-of-plane displacement, the same principle could be applied to other vibrating instruments (biopsy needles, catheters, ablation tools) and possibly to other imaging modalities that detect motion, not just ultrasound.
  • A natural next step is to combine the vibration metric with image-based detection: use the vibration signal for coarse alignment when the needle is invisible, then switch to image guidance for fine positioning when the needle re-enters the plane.
  • The full-text content supplied alongside this paper is a different manuscript (on C-to-Rust translation); this extraction is grounded solely in the title and abstract, since no matching full text was available.
  • The experiments are on ex-vivo tissue; a key open question is whether living tissue motion and variable acoustic coupling change the energy-misalignment relationship, which would require in-vivo validation.
Share X Bluesky LinkedIn Reddit HN

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

4 major / 2 minor

Summary. The manuscript, as submitted, consists of an abstract claiming a vibration-based energy metric for restoring needle-plane alignment in robotic ultrasound, with reported ex-vivo porcine translational/rotational errors, followed by a full text that is an unrelated paper on LLM-based C-to-Rust translation (arXiv:2508.06926). The full text contains no derivation or definition of the proposed metric, no description of the vibration actuation or ultrasound signal processing, no control law, and no experimental protocol for the robotic ultrasound system. The central claims rest entirely on the abstract and cannot be verified or reproduced from the submitted material.

Significance. If the central claim were substantiated, the proposed metric would be a valuable contribution: a visibility-free feedback signal for out-of-plane needle misalignment could overcome a known limitation of image-based needle detection in robotic ultrasound-guided procedures. The ex-vivo error values (0.41±0.27 mm translational, 0.51±0.19 degrees rotational) are plausible targets for clinical relevance. However, the submission provides no derivations, no algorithmic details, no experimental protocol, no data, and no code; in its current form it offers no verifiable evidence for these claims.

major comments (4)
  1. [Full Text (entire)] The submitted full text is arXiv:2508.06926, 'Integrating Rules and Semantics for LLM-Based C-to-Rust Translation.' It contains no mention of ultrasound, needle alignment, vibration energy, or ex-vivo experiments. None of the Abstract's claims—the energy metric, its out-of-plane effectiveness, the control strategy, or the reported errors—appears in the body. There is no definition or derivation of the metric, no signal-processing pipeline, no control law, and no experimental protocol. The central assertion is therefore unsupported by any manuscript content.
  2. [Abstract (load-bearing premise)] The claim that the metric 'remains effective even when the needle is fully out of plane' requires a model or measurement showing that the vibration energy measured at the transducer is a monotonic and separable function of out-of-plane misalignment, and that it is not confounded by tissue attenuation, speckle, or tissue motion. No such derivation or characterization is provided. This is not an internal inconsistency; it is a missing load-bearing element that cannot be evaluated from the text.
  3. [Abstract/Experimental results] The reported translational error (0.41±0.27 mm) and rotational error (0.51±0.19 degrees) are given without any experimental protocol: no description of the dual-arm system, ex-vivo tissue preparation, ground-truth measurement, number of trials, or statistical methodology. The full text's experimental sections (Tables II–V, Figs. 4–9) report C-to-Rust translation metrics (CA, CSR, UR, ULR) and are unrelated. The numerical claims cannot be traced to any reproducible procedure.
  4. [Calibration/circularity risk (unspecified)] No information is given about how the vibration frequency and amplitude, energy-metric thresholds, or control gains were selected. If these were tuned on the same ex-vivo data used for the final error evaluation, the reported results could partially reflect fitting to the data. The absence of any calibration or validation split makes this concern unresolvable from the current submission.
minor comments (2)
  1. [Abstract] The abstract uses 'pm' where '±' is intended; this is a minor typographical issue but should be corrected in any revised version.
  2. [Metadata/Manuscript consistency] The author list, title, and subject area of the full text do not match those of the abstract. At minimum, the manuscript should be self-consistent; as submitted it appears to be a different paper entirely.

Circularity Check

0 steps flagged

No circularity identified: the supplied full text is an unrelated C-to-Rust paper, so the claimed ultrasound method's derivation is not available for circularity inspection.

full rationale

The abstract describes arXiv:2508.06921, a robotic ultrasound paper proposing a vibration-based energy metric and reporting ex-vivo needle-alignment errors. However, the supplied full text is arXiv:2508.06926, 'Integrating Rules and Semantics for LLM-Based C-to-Rust Translation.' The ultrasound paper's derivation chain — including the definition of the energy metric, the claimed monotonic/sensitivity properties, the control law, and the experimental protocol — is entirely absent from the supplied text. Circularity requires exhibiting a specific reduction of a claimed result to its inputs by construction (e.g., Eq. X = Eq. Y by definition, or a fitted parameter renamed as a prediction). With no method text for the ultrasound work, no such reduction can be exhibited, and the reported errors cannot be traced to any methodology. This is a verifiability/mismatch problem, not a circularity finding. For the C-to-Rust manuscript that is actually present, IRENE's claims are empirical evaluations against baselines on public and industrial datasets; the rule hints, retrieved examples, and summaries are inputs to the LLM prompt, while CA/CSR/UR/ULR are measured outputs. No derived quantity is defined in terms of itself, and no load-bearing self-citation chain forces the result. Therefore no significant circularity is found.

Axiom & Free-Parameter Ledger

2 free parameters · 3 axioms · 1 invented entities

The central claim rests on the vibration energy metric behaving as a faithful, monotonic misalignment signal, on ex-vivo tissue standing in for living tissue, and on the metric being independent of the image noise that motivates the work. Two families of free parameters (vibration settings, metric-to-control thresholds) are unstated and likely tuned. No new physical entity is introduced beyond the proposed metric.

free parameters (2)
  • Needle vibration frequency and amplitude
    The vibration parameters are not stated in the abstract; they must be chosen and are likely tuned for tissue response and ultrasound frame rate.
  • Energy metric thresholds and control gains
    The control strategy must convert the energy metric into probe motions; thresholds and gains are not specified in the abstract and are likely tuned on the same ex-vivo data.
axioms (3)
  • domain assumption Vibration energy is a monotonic function of imaging-plane to needle-plane misalignment
    The whole controller rests on the metric being a valid proxy for plane misalignment; this is asserted in the abstract ('remains effective even when the needle is fully out of plane') without derivation.
  • domain assumption Ex-vivo porcine tissue approximates in-vivo tissue for vibration-energy measurements
    Experiments are ex-vivo only (abstract); transfer to in-vivo conditions (perfusion, motion, heterogeneity) is assumed without evidence.
  • domain assumption Speckle noise and needle-like artifacts do not dominate the vibration signal
    The abstract motivates the method by robustness to speckle/noise and low resolution; the separability of the vibration signal from these confounders is assumed.
invented entities (1)
  • Vibration-based energy metric no independent evidence
    purpose: A scalar signal used to drive probe repositioning when the needle is out of the imaging plane
    The metric is proposed in this paper; its calibration and whether it is an independent physical measurement (e.g., vibration amplitude from ultrasound data) versus a fitted score is not shown in the abstract.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Vibration-Based Energy Metric for Restoring Needle Alignment in Autonomous Robotic Ultrasound." pith.science (2026). https://pith.science/paper/JAT2D6MG

@misc{pith2026250806921,
  author       = {Pith},
  title        = {Pith review of: Vibration-Based Energy Metric for Restoring Needle Alignment in Autonomous Robotic Ultrasound},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JAT2D6MG}},
  note         = {Machine review of arXiv:2508.06921}
}
Share X Bluesky LinkedIn Reddit HN
abstract

Precise needle alignment is essential for percutaneous needle insertion in robotic ultrasound-guided procedures. However, inherent challenges such as speckle noise, needle-like artifacts, and low image resolution make robust needle detection difficult, particularly when visibility is reduced or lost. In this paper, we propose a method to restore needle alignment when the ultrasound imaging plane and the needle insertion plane are misaligned. Unlike many existing approaches that rely heavily on needle visibility in ultrasound images, our method uses a more robust feature by periodically vibrating the needle using a mechanical system. Specifically, we propose a vibration-based energy metric that remains effective even when the needle is fully out of plane. Using this metric, we develop a control strategy to reposition the ultrasound probe in response to misalignments between the imaging plane and the needle insertion plane in both translation and rotation. Experiments conducted on ex-vivo porcine tissue samples using a dual-arm robotic ultrasound-guided needle insertion system demonstrate the effectiveness of the proposed approach. The experimental results show the translational error of 0.41$\pm$0.27 mm and the rotational error of 0.51$\pm$0.19 degrees.

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

42 extracted references · 32 canonical work pages

  1. [1]

    Improving automatic c-to-rust translation with static analysis,

    J. Hong, “Improving automatic c-to-rust translation with static analysis,” inICSE Companion, pp. 273–277, IEEE, 2023

    work page 2023

  2. [2]

    Linux kernel vulnerabilities: state-of-the-art defenses and open problems,

    H. Chen, Y . Mao, X. Wang, D. Zhou, N. Zeldovich, and M. F. Kaashoek, “Linux kernel vulnerabilities: state-of-the-art defenses and open problems,” inAPSys, p. 5, ACM, 2011

    work page 2011

  3. [3]

    The matter of heartbleed,

    Z. Durumeric, J. Kasten, D. Adrian, J. A. Halderman, M. D. Bailey, F. Li, N. Weaver, J. Amann, J. Beekman, M. Payer, and V . Paxson, “The matter of heartbleed,” inInternet Measurement Conference, pp. 475–488, ACM, 2014

    work page 2014

  4. [4]

    Rewriting a browser component in rust

    D. Herman, “Rewriting a browser component in rust.” https://hacks. mozilla.org/2019/02/rewriting-a-browser-component-in-rust/, 2019. Ac- cessed: 2025-05-19

    work page 2019

  5. [5]

    Nova: a rust-based gpu driver

    Rust for Linux, “Nova: a rust-based gpu driver.” https://rust-for-linux. com/nova-gpu-driver, 2024. Accessed: 2025-05-19

    work page 2024

  6. [6]

    Rust in the android platform

    Google Security Blog, “Rust in the android platform.” https://security. googleblog.com/2021/04/rust-in-android-platform.html, 2021. Ac- cessed: 2025-05-19

    work page 2021

  7. [7]

    Ownership guided C to rust translation,

    H. Zhang, C. David, Y . Yu, and M. Wang, “Ownership guided C to rust translation,” inCAV (3), vol. 13966 ofLecture Notes in Computer Science, pp. 459–482, Springer, 2023

    work page 2023

  8. [8]

    To tag, or not to tag: Translating c’s unions to rust’s tagged unions,

    J. Hong and S. Ryu, “To tag, or not to tag: Translating c’s unions to rust’s tagged unions,” inASE, pp. 40–52, ACM, 2024

    work page 2024

  9. [9]

    In rust we trust - A transpiler from unsafe C to safer rust,

    M. Ling, Y . Yu, H. Wu, Y . Wang, J. R. Cordy, and A. E. Hassan, “In rust we trust - A transpiler from unsafe C to safer rust,” inICSE-Companion, pp. 354–355, ACM/IEEE, 2022

    work page 2022

  10. [10]

    Immunant, “C2rust.” https://github.com/immunant/c2rust, 2022

    work page 2022

  11. [11]

    Clang: A c language family frontend for llvm

    LLVM Project, “Clang: A c language family frontend for llvm.” https: //clang.llvm.org/. Accessed: 2025-05-26

    work page 2025

  12. [12]

    LLVM: A compilation framework for lifelong program analysis & transformation,

    C. Lattner and V . S. Adve, “LLVM: A compilation framework for lifelong program analysis & transformation,” in2nd IEEE / ACM International Symposium on Code Generation and Optimization (CGO 2004), 20-24 March 2004, San Jose, CA, USA, pp. 75–88, IEEE Computer Society, 2004

    work page 2004

  13. [13]

    Concrat: An automatic c-to-rust lock API translator for concurrent programs,

    J. Hong and S. Ryu, “Concrat: An automatic c-to-rust lock API translator for concurrent programs,” in45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023, pp. 716–728, IEEE, 2023

    work page 2023

  14. [14]

    Ownership guided C to rust translation,

    H. Zhang, C. David, Y . Yu, and M. Wang, “Ownership guided C to rust translation,” inComputer Aided Verification - 35th International Conference, CAV 2023, Paris, France, July 17-22, 2023, Proceedings, Part III(C. Enea and A. Lal, eds.), vol. 13966 ofLecture Notes in Computer Science, pp. 459–482, Springer, 2023

    work page 2023

  15. [15]

    Translating C to safer rust,

    M. Emre, R. Schroeder, K. Dewey, and B. Hardekopf, “Translating C to safer rust,”Proc. ACM Program. Lang., vol. 5, no. OOPSLA, pp. 1–29, 2021

    work page 2021

  16. [16]

    Aliasing limits on translating C to safe rust,

    M. Emre, P. Boyland, A. Parekh, R. Schroeder, K. Dewey, and B. Hard- ekopf, “Aliasing limits on translating C to safe rust,”Proc. ACM Program. Lang., vol. 7, no. OOPSLA1, pp. 551–579, 2023

    work page 2023

  17. [17]

    Improving automatic c-to-rust translation with static analysis,

    J. Hong, “Improving automatic c-to-rust translation with static analysis,” in45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, Melbourne, Australia, May 14-20, 2023, pp. 273–277, IEEE, 2023

    work page 2023

  18. [18]

    Context-aware code segmenta- tion for c-to-rust translation using large language models,

    M. Shiraishi and T. Shinagawa, “Context-aware code segmenta- tion for c-to-rust translation using large language models,”CoRR, vol. abs/2409.10506, 2024

    arXiv 2024

  19. [21]

    Vert: Verified equivalent rust transpilation with large language models as few-shot learners,

    A. Z. H. Yang, Y . Takashima, B. Paulsen, J. Dodds, and D. Kroening, “Vert: Verified equivalent rust transpilation with large language models as few-shot learners,” 2024

    work page 2024

  20. [22]

    Towards translating real-world code with llms: A study of translating to rust,

    H. F. Eniser, H. Zhang, C. David, M. Wang, M. Christakis, B. Paulsen, J. Dodds, and D. Kroening, “Towards translating real-world code with llms: A study of translating to rust,”CoRR, vol. abs/2405.11514, 2024

    Pith/arXiv arXiv 2024

  21. [23]

    C2saferrust: Trans- forming C projects into safer rust with neurosymbolic techniques,

    V . Nitin, R. Krishna, L. L. do Valle, and B. Ray, “C2saferrust: Trans- forming C projects into safer rust with neurosymbolic techniques,” CoRR, vol. abs/2501.14257, 2025

    arXiv 2025

  22. [24]

    Llm-driven multi-step translation from C to rust using static analysis,

    T. Zhou, H. Lin, S. Jha, M. Christodorescu, K. Levchenko, and V . Chan- drasekaran, “Llm-driven multi-step translation from C to rust using static analysis,”CoRR, vol. abs/2503.12511, 2025

    arXiv 2025

  23. [25]

    Exploring and unleashing the power of large language models in automated code translation,

    Z. Yang, F. Liu, Z. Yu, J. W. Keung, J. Li, S. Liu, Y . Hong, X. Ma, Z. Jin, and G. Li, “Exploring and unleashing the power of large language models in automated code translation,”Proc. ACM Softw. Eng., vol. 1, no. FSE, pp. 1585–1608, 2024

    work page 2024

  24. [26]

    Xcodeeval: An execution-based large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval,

    M. A. M. Khan, M. S. Bari, X. D. Long, W. Wang, M. R. Parvez, and S. Joty, “Xcodeeval: An execution-based large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval,” inProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand...

    work page 2024

  25. [27]

    Repository-level code translation benchmark targeting rust,

    G. Ou, M. Liu, Y . Chen, X. Peng, and Z. Zheng, “Repository-level code translation benchmark targeting rust,”CoRR, vol. abs/2411.13990, 2024

    arXiv 2024

  26. [28]

    Replication package of irene

    IRENE, “Replication package of irene.” https://anonymous.4open. science/r/IRENE-2E0E, 2025

    work page 2025

  27. [29]

    The probabilistic relevance frame- work: BM25 and beyond,

    S. E. Robertson and H. Zaragoza, “The probabilistic relevance frame- work: BM25 and beyond,”Found. Trends Inf. Retr., vol. 3, no. 4, pp. 333–389, 2009

    work page 2009

  28. [30]

    Language models are few-shot learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert- V oss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Am...

    work page 2020

  29. [31]

    A survey on RAG meeting llms: Towards retrieval-augmented large language models,

    W. Fan, Y . Ding, L. Ning, S. Wang, H. Li, D. Yin, T. Chua, and Q. Li, “A survey on RAG meeting llms: Towards retrieval-augmented large language models,” inKDD, pp. 6491–6501, ACM, 2024

    work page 2024

  30. [32]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V . Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” inAdvances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022(...

    work page 2022

  31. [33]

    Deepseek-coder: When the large language model meets programming - the rise of code intelligence,

    D. Guo, Q. Zhu, D. Yang, Z. Xie, K. Dong, W. Zhang, G. Chen, X. Bi, Y . Wu, Y . K. Li, F. Luo, Y . Xiong, and W. Liang, “Deepseek-coder: When the large language model meets programming - the rise of code intelligence,”CoRR, vol. abs/2401.14196, 2024

    Pith/arXiv arXiv 2024

  32. [34]

    Qwen2. 5-coder technical report,

    B. Hui, J. Yang, Z. Cui, J. Yang, D. Liu, L. Zhang, T. Liu, J. Zhang, B. Yu, K. Dang,et al., “Qwen2. 5-coder technical report,”arXiv preprint arXiv:2409.12186, 2024

    Pith/arXiv arXiv 2024

  33. [35]

    Code llama: Open foundation models for code,

    B. Rozi `ere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y . Adi, J. Liu, T. Remez, J. Rapin, A. Kozhevnikov, I. Evtimov, J. Bitton, M. Bhatt, C. Canton-Ferrer, A. Grattafiori, W. Xiong, A. D ´efossez, J. Copet, F. Azhar, H. Touvron, L. Martin, N. Usunier, T. Scialom, and G. Synnaeve, “Code llama: Open foundation models for code,”CoRR, vol. ab...

    Pith/arXiv arXiv 2023

  34. [36]

    Hugging face: The platform for machine learning and nlp

    Hugging Face, Inc., “Hugging face: The platform for machine learning and nlp.” https://huggingface.co/. Accessed: 2025-05-26

    work page 2025

  35. [37]

    Pytorch: An open source machine learning framework

    PyTorch Contributors, “Pytorch: An open source machine learning framework.” https://pytorch.org/. Accessed: 2025-05-26

    work page 2025

  36. [38]

    What makes good in-context demonstrations for code intelligence tasks with llms?,

    S. Gao, X. Wen, C. Gao, W. Wang, H. Zhang, and M. R. Lyu, “What makes good in-context demonstrations for code intelligence tasks with llms?,” in38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023, pp. 761–773, IEEE, 2023

    work page 2023

  37. [39]

    Mining historic query trails to label long and rare search engine queries,

    P. Bailey, R. W. White, H. Liu, and G. Kumaran, “Mining historic query trails to label long and rare search engine queries,”ACM Trans. Web, vol. 4, no. 4, pp. 15:1–15:27, 2010

    work page 2010

  38. [40]

    Fullstack bench: Evaluating llms as full stack coders,

    Y . Cheng, J. Chen, J. Chen, L. Chen, L. Chen, W. Chen, Z. Chen, S. Geng, A. Li, B. Li, B. Li, L. Li, B. Liu, J. Liu, K. Liu, Q. Liu, S. Liu, S. Liu, T. Liu, T. Liu, Y . Liu, R. Long, J. Mai, G. Ning, Z. Y . Peng, K. Shen, J. Su, J. Su, T. Sun, Y . Sun, Y . Tao, G. Wang, S. Wang, X. Wang, Y . Wang, Z. Wang, J. Xia, L. Xiang, X. Xiao, Y . Xiao, C. Xi, S. X...

    Pith/arXiv arXiv 2024

  39. [41]

    Don’t write, but return: Replacing output pa- rameters with algebraic data types in c-to-rust translation,

    J. Hong and S. Ryu, “Don’t write, but return: Replacing output pa- rameters with algebraic data types in c-to-rust translation,”Proc. ACM Program. Lang., vol. 8, no. PLDI, pp. 716–740, 2024

    work page 2024

  40. [42]

    Syzygy: Dual code-test C to (safe) rust translation using llms and dynamic analysis,

    M. Shetty, N. Jain, A. Godbole, S. A. Seshia, and K. Sen, “Syzygy: Dual code-test C to (safe) rust translation using llms and dynamic analysis,” CoRR, vol. abs/2412.14234, 2024

    Pith/arXiv arXiv 2024

  41. [43]

    Type-migrating c-to-rust translation using a large language model,

    J. Hong and S. Ryu, “Type-migrating c-to-rust translation using a large language model,”Empir. Softw. Eng., vol. 30, no. 1, p. 3, 2025

    work page 2025

  42. [44]

    Translating C to rust: Lessons from a user study,

    R. Li, B. Wang, T. Li, P. Saxena, and A. Kundu, “Translating C to rust: Lessons from a user study,” in32nd Annual Network and Distributed System Security Symposium, NDSS 2025, San Diego, California, USA, February 24-28, 2025, The Internet Society, 2025

    work page 2025

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.