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REVIEW 4 major objections 3 minor 40 references

A single transformer learns to play 3D games from pixels alone and transfers to new titles.

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 →

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Pixels2Play-0.1 is a decoder-only transformer trained via behavior cloning and inverse-dynamics-imputed actions to play 3D games from pixels, with only qualitative results reported.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The abstract describes a plausible gameplay agent, but the uploaded manuscript body is an unrelated protocol-fuzzing paper—so there is no P2P0.1 paper here to review. the 4 major comments →

arxiv 2508.14295 v1 pith:3HSKSHO5 submitted 2025-08-19 cs.CV cs.AIcs.LG

Pixels to Play: A Foundation Model for 3D Gameplay

Yuguang Yue , Chris Green , Samuel Hunt , Irakli Salia , Wenzhe Shi , Jonathan J Hunt This is my paper
classification cs.CV cs.AIcs.LG
keywords Pixels2Play-0.13D gameplaybehavior cloninginverse dynamicsdecoder-only transformervideo game agentsfoundation modelimitation learning
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

Pixels2Play-0.1 (P2P0.1) is trained to play 3D games using only the same pixel stream a human player sees. The paper claims that behavior cloning on instrumented human demonstrations, supplemented by unlabeled public gameplay videos whose actions are filled in by an inverse-dynamics model, yields a general gameplay agent that extends to new titles with minimal per-game engineering. The reported evidence is qualitative: competent play on simple Roblox titles and classic MS-DOS games, plus ablation results on the unlabeled data. If the claim holds, it offers a route to game-playing agents that do not need game-specific APIs, internal state, or per-title reward engineering.

Core claim

P2P0.1's central claim is that a single decoder-only transformer, trained end-to-end from raw pixels to autoregressive action sequences, can exhibit human-like play across multiple 3D games and generalize to a new title with little game-specific engineering. The training pipeline combines labeled demonstrations from instrumented human gameplay with much larger amounts of unlabeled public video; an inverse-dynamics model imputes the missing actions in the unlabeled video, so the behavior-cloning objective has a complete (frame, action) stream. The same pixel input that human players see keeps the model game-agnostic and deployable on a single consumer GPU.

What carries the argument

The load-bearing object is the P2P0.1 agent: a decoder-only transformer with autoregressive action output that maps pixels to gameplay actions. It is trained end-to-end by behavior cloning, with an inverse-dynamics model supplying actions for unlabeled public videos; the transformer's autoregressive action head is what lets it handle a large action space while staying latency-friendly on consumer hardware.

Load-bearing premise

The load-bearing premise is that the inverse-dynamics model, trained on a limited set of labeled demonstrations, can correctly impute actions for arbitrary unlabeled videos across different games; if those imputed actions are systematically wrong for new games, the behavior-cloning signal degrades and the generalization claim collapses.

What would settle it

Take a set of games not used to train the inverse-dynamics model, record human play with ground-truth actions, and run the inverse-dynamics model on the frames; if its action predictions are near chance on those held-out games, the unlabeled-video training signal is too noisy to support P2P0.1's generalization claim. A second check: train P2P0.1 with and without the unlabeled imputed videos and compare progress on a held-out title; no improvement would falsify the paper's core data strategy.

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

If this is right

  • Game developers could add AI teammates or controllable NPCs without instrumenting each title: unlabeled gameplay video becomes usable training data.
  • A pixel-only agent can be dropped into a new game without APIs, memory inspection, or reward design, lowering the engineering cost per title.
  • The same model could serve latency-sensitive applications on a single consumer GPU, such as live-streaming assistants or assistive game testers.
  • Scaling up labeled and unlabeled data, as the paper outlines, is the direct path toward expert-level, text-conditioned control.
  • If behavior cloning from pixels generalizes, a shared foundation model could replace per-game agents across many titles.

Where Pith is reading between the lines

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

  • The decisive test for the scaling story is whether the inverse-dynamics model imputes accurate actions on games absent from its labeled training set; the abstract does not yet measure that transfer.
  • The paper leaves open whether imitation alone can exceed human-level play; combining P2P-style imitation with self-play or reinforcement learning is a natural next step the paper does not explore.
  • Competent play on simple Roblox and MS-DOS titles is suggestive but not a benchmark; a public multi-game evaluation with task-success and human-likeness metrics would make the generalization claim measurable.
  • The body text attached to this record describes a separate network-protocol-fuzzing system; the extraction above follows the paper's titled abstract, not that unrelated text.
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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 / 3 minor

Summary. The arXiv submission presents, in its abstract, a method called Pixels2Play-0.1 (P2P0.1): a decoder-only transformer trained end-to-end with behavior cloning on labeled human demonstrations plus unlabeled public gameplay videos whose actions are imputed by an inverse-dynamics model. The abstract claims that P2P0.1 'learns to play a wide range of 3D video games with recognizable human-like behavior' and 'generalize[s] to new titles with minimal game-specific engineering,' supported by qualitative results on simple Roblox and classic MS-DOS titles. The full text of the submission, however, is an entirely different paper: 'MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing.' It contains no mention of P2P0.1, gameplay, inverse-dynamics models, behavior cloning, Roblox, MS-DOS, or any related content. The manuscript therefore provides no methods, ablations, tables, figures, or other evidence for the central claim.

Significance. If the abstract's claims were backed by a real system description, the work could be significant for scalable, pixel-only game agents and for using unlabeled video via inverse-dynamics imputation. The stated motivation—an agent that uses the same pixel stream as humans and requires minimal game-specific engineering—is reasonable, and the proposed data recipe (behavior cloning plus inverse-dynamics-imputed actions) is a plausible direction. However, in its current form the manuscript contains no verifiable contribution. There is no architecture description, no training details, no datasets, no baseline comparisons, no quantitative metrics, no error bars, no code, and no qualitative results. The only assessable content is the abstract itself, whose claims are therefore unsupported. This is a fundamental evidential failure, not a matter of presentation.

major comments (4)
  1. [Full Text (entire manuscript body)] The body of the submission (Sections I–VIII, Tables I–III, References [1]–[40]) is a different paper, 'MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing.' It contains no mention of P2P0.1, gameplay, inverse dynamics, behavior cloning, Roblox, MS-DOS, or any related content. Consequently, the abstract's central claim—that P2P0.1 'learns to play a wide range of 3D video games'—has no methods or results in the manuscript. This is not a local defect; it removes the evidential basis for the paper entirely.
  2. [Abstract, 'ablations on unlabeled data' and 'qualitative results'] The abstract promises qualitative results, ablations on unlabeled data, and scaling/evaluation steps. None appear anywhere in the full text. There are no quantitative metrics, baselines, error bars, or reproducibility artifacts for P2P0.1. Even the qualitative claim of 'competent play' cannot be assessed, since no gameplay videos, screenshots, or task-completion statistics are provided.
  3. [Abstract, 'impute actions via an inverse-dynamics model'] The behavior-cloning training signal on unlabeled videos depends entirely on the inverse-dynamics model's ability to infer actions accurately across games. The manuscript provides no details on this model, its training data, or its transfer accuracy to unseen games. If the inverse-dynamics model was trained on the same labeled demonstrations that define the behavior-cloning objective, the imputed 'unlabeled' supervision would inherit distributional biases from those demonstrations. No experiment tests the imputation quality on held-out games, so the generalization claim rests on an unstated assumption.
  4. [Abstract, 'wide range of 3D video games'] The only reported evidence is 'qualitative results showing competent play across simple Roblox and classic MS-DOS titles.' This is a narrow, self-selected set, and the claim of a 'wide range' of games and 'human-like behavior' is not supported without a defined evaluation protocol, baselines, human comparisons, or held-out generalization tests. The title and abstract overstate the evidentiary basis.
minor comments (3)
  1. [Title and Abstract vs. Full Text] The term 'P2P0.1' appears only in the abstract; the body uses none of the paper's terminology. The page-1 arXiv identifier 'arXiv:2508.14300v1 [cs.CR]' also belongs to the MultiFuzz paper, consistent with a file/submission mismatch.
  2. [Abstract, 'latency-friendly on a single consumer GPU'] No latency, throughput, or hardware measurements are reported anywhere, so this performance claim is unverifiable.
  3. [References] The reference list is entirely for the protocol-fuzzing paper and contains no citations to related work on game-playing agents (e.g., imitation learning in games, inverse dynamics for action inference, or prior foundation models for games).

Circularity Check

0 steps flagged

No significant circularity: the claimed gameplay paper's methods/results are absent from the body, making the central claim unsupported rather than circular

full rationale

The abstract of arXiv:2508.14295 claims a foundation model for 3D gameplay (P2P0.1) trained by behavior cloning on labeled demonstrations and unlabeled public videos with inverse-dynamics imputed actions. The supplied full text, however, is an unrelated paper on protocol fuzzing (MultiFuzz), containing no methods, ablations, or qualitative results for P2P0.1. Under the hard rules, circularity can only be flagged by quoting a specific reduction of a claimed derivation to its inputs. No such reduction exists here: there are no equations or experimental descriptions for the gameplay model, so there is no derivation chain to inspect. The only potential concern is that the inverse-dynamics model used to impute actions for unlabeled videos might be trained on the same labeled demonstrations, biasing the unlabeled supervision. But the abstract does not specify the ID model's training distribution, and even if it were trained on the labeled set, using imputed actions as training targets for behavior cloning is the stated method, not a hidden circularity unless a fitted parameter is then reported as an independent prediction. The MultiFuzz text contains self-citations, but they are not load-bearing to any P2P0.1 claim. The central problem is missing evidence, which is a correctness/verifiability issue, not circular reasoning. Accordingly, the circularity score is 0.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

Only the abstract is available for review. No explicit free parameters or invented entities are described. The three axioms above are load-bearing assumptions from the training pipeline described in the abstract.

axioms (3)
  • ad hoc to paper The inverse-dynamics model trained on labeled demonstrations can accurately infer actions for arbitrary unlabeled public gameplay videos.
    The abstract relies on this to generate training supervision at scale, but provides no evidence that the imputations are correct across games.
  • domain assumption A decoder-only transformer with auto-regressive discrete action output can represent the action spaces of multiple distinct 3D games.
    The architecture is presented as handling the large action space, but the abstract gives no detail on how a shared action space is defined or mapped across games.
  • domain assumption Human demonstrations define human-like behavior and are sufficient as supervision for generalizable gameplay.
    Behavior cloning assumes the demonstrated policy is the target; no analysis of demonstration quality or coverage is provided in the abstract.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Pixels to Play: A Foundation Model for 3D Gameplay." pith.science (2026). https://pith.science/paper/3HSKSHO5

@misc{pith2026250814295,
  author       = {Pith},
  title        = {Pith review of: Pixels to Play: A Foundation Model for 3D Gameplay},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HSKSHO5}},
  note         = {Machine review of arXiv:2508.14295}
}
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read the original abstract

We introduce Pixels2Play-0.1 (P2P0.1), a foundation model that learns to play a wide range of 3D video games with recognizable human-like behavior. Motivated by emerging consumer and developer use cases - AI teammates, controllable NPCs, personalized live-streamers, assistive testers - we argue that an agent must rely on the same pixel stream available to players and generalize to new titles with minimal game-specific engineering. P2P0.1 is trained end-to-end with behavior cloning: labeled demonstrations collected from instrumented human game-play are complemented by unlabeled public videos, to which we impute actions via an inverse-dynamics model. A decoder-only transformer with auto-regressive action output handles the large action space while remaining latency-friendly on a single consumer GPU. We report qualitative results showing competent play across simple Roblox and classic MS-DOS titles, ablations on unlabeled data, and outline the scaling and evaluation steps required to reach expert-level, text-conditioned control.

discussion (0)

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Reference graph

Works this paper leans on

40 extracted references · 27 canonical work pages · 3 internal anchors

  1. [1]

    Sutton, A

    M. Sutton, A. Greene, and P. Amini, Fuzzing: brute force vulnerability discovery. Pearson Education, 2007

    2007

  2. [2]

    A survey of network protocol fuzzing: Model, techniques and directions,

    S. Jiang, Y . Zhang, J. Li, H. Yu, L. Luo, and G. Sun, “A survey of network protocol fuzzing: Model, techniques and directions,” arXiv preprint arXiv:2402.17394, 2024

    Pith/arXiv arXiv 2024

  3. [3]

    A survey of automatic protocol reverse engineering tools,

    J. Narayan, S. K. Shukla, and T. C. Clancy, “A survey of automatic protocol reverse engineering tools,” ACM Computing Surveys (CSUR) , vol. 48, no. 3, pp. 1–26, 2015

    work page 2015

  4. [4]

    State selection algorithms and their impact on the performance of stateful network protocol fuzzing,

    D. Liu, V .-T. Pham, G. Ernst, T. Murray, and B. I. Rubinstein, “State selection algorithms and their impact on the performance of stateful network protocol fuzzing,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2022, pp. 720–730

    work page 2022

  5. [5]

    Evaluating large language models trained on code,

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockman et al., “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021

    Pith/arXiv arXiv 2021

  6. [6]

    Language models can solve computer tasks,

    G. Kim, P. Baldi, and S. McAleer, “Language models can solve computer tasks,” Advances in Neural Information Processing Systems , vol. 36, pp. 39 648–39 677, 2023

    work page 2023

  7. [7]

    Large language models based fuzzing techniques: A survey,

    L. Huang, P. Zhao, H. Chen, and L. Ma, “Large language models based fuzzing techniques: A survey,” arXiv preprint arXiv:2402.00350 , 2024

    Pith/arXiv arXiv 2024

  8. [8]

    Generative ai and large language models for cyber security: All insights you need,

    M. A. Ferrag, F. Alwahedi, A. Battah, B. Cherif, A. Mechri, and N. Tihanyi, “Generative ai and large language models for cyber security: All insights you need,” Available at SSRN 4853709 , 2024

    work page 2024

  9. [9]

    Large language model guided protocol fuzzing,

    R. Meng, M. Mirchev, M. B ¨ohme, and A. Roychoudhury, “Large language model guided protocol fuzzing,” in Proceedings of the 31st Annual Network and Distributed System Security Symposium (NDSS) , 2024

    work page 2024

  10. [10]

    Dense x retrieval: What retrieval granularity should we use?

    T. Chen, H. Wang, S. Chen, W. Yu, K. Ma, X. Zhao, H. Zhang, and D. Yu, “Dense x retrieval: What retrieval granularity should we use?” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024, pp. 15 159–15 177

    work page 2024

  11. [11]

    Retrieval- augmented generation for knowledge-intensive nlp tasks,

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt ¨aschel et al. , “Retrieval- augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 9459–9474, 2020

    2020

  12. [12]

    React: Synergizing reasoning and acting in language models,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629, 2022

    Pith/arXiv arXiv 2022

  13. [13]

    The art, science, and engineering of fuzzing: A survey,

    V . J. Man `es, H. Han, C. Han, S. K. Cha, M. Egele, E. J. Schwartz, and M. Woo, “The art, science, and engineering of fuzzing: A survey,” IEEE Transactions on Software Engineering , vol. 47, no. 11, pp. 2312–2331, 2019

    work page 2019

  14. [14]

    A survey on the development of network protocol fuzzing techniques,

    Z. Zhang, H. Zhang, J. Zhao, and Y . Yin, “A survey on the development of network protocol fuzzing techniques,” Electronics, vol. 12, no. 13, p. 2904, 2023

    work page 2023

  15. [15]

    Attention is all you need,

    A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017

    2017

  16. [16]

    Chatphishdetector: Detecting phishing sites using large language models,

    T. Koide, H. Nakano, and D. Chiba, “Chatphishdetector: Detecting phishing sites using large language models,” IEEE Access, 2024

    work page 2024

  17. [17]

    Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning

    Y . Maklad, F. Wael, W. Elsersy, and A. Hamdi, “Retrieval augmented generation based llm evaluation for protocol state machine inference with chain-of-thought reasoning,” arXiv preprint arXiv:2502.15727 , 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    Harnessing large lan- guage models for seed generation in greybox fuzzing,

    W. Shi, Y . Zhang, X. Xing, and J. Xu, “Harnessing large lan- guage models for seed generation in greybox fuzzing,” arXiv preprint arXiv:2411.18143, 2024

    Pith/arXiv arXiv 2024

  19. [19]

    Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,

    C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2023, pp. 919–931

    work page 2023

  20. [20]

    Erpa: Efficient rpa model integrating ocr and llms for intelligent document processing,

    O. H. Abdellaif, A. N. Hassan, and A. Hamdi, “Erpa: Efficient rpa model integrating ocr and llms for intelligent document processing,” in 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024, pp. 295–300

    work page 2024

  21. [21]

    LMRPA: Large Language Model-Driven Efficient Robotic Process Automation for OCR

    O. H. Abdellaif, A. Nader, and A. Hamdi, “Lmrpa: Large lan- guage model-driven efficient robotic process automation for ocr,” arXiv preprint arXiv:2412.18063, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  22. [22]

    LMV-RPA: Large Model Voting-based Robotic Process Automation

    O. Abdellatif, A. Ayman, and A. Hamdi, “Lmv-rpa: Large model voting- based robotic process automation,” arXiv preprint arXiv:2412.17965 , 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  23. [23]

    Llm multi-agent systems: Challenges and open problems,

    S. Han, Q. Zhang, Y . Yao, W. Jin, and Z. Xu, “Llm multi-agent systems: Challenges and open problems,” arXiv preprint arXiv:2402.03578, 2024

    Pith/arXiv arXiv 2024

  24. [24]

    Pentestagent: Incorporating llm agents to automated penetration testing,

    X. Shen, L. Wang, Z. Li, Y . Chen, W. Zhao, D. Sun, J. Wang, and W. Ruan, “Pentestagent: Incorporating llm agents to automated penetration testing,” arXiv preprint arXiv:2411.05185 , 2024

    Pith/arXiv arXiv 2024

  25. [25]

    Ics protocol fuzzing: Coverage guided packet crack and generation,

    Z. Luo, F. Zuo, Y . Shen, X. Jiao, W. Chang, and Y . Jiang, “Ics protocol fuzzing: Coverage guided packet crack and generation,” in 2020 57th ACM/IEEE Design Automation Conference (DAC) . IEEE, 2020, pp. 1–6

    work page 2020

  26. [26]

    Bbuzz: A bit-aware fuzzing framework for network protocol systematic reverse engineering and analysis,

    B. Blumbergs and R. Vaarandi, “Bbuzz: A bit-aware fuzzing framework for network protocol systematic reverse engineering and analysis,” in MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM). IEEE, 2017, pp. 707–712

    work page 2017

  27. [27]

    Pulsar: Stateful black-box fuzzing of proprietary network protocols,

    H. Gascon, C. Wressnegger, F. Yamaguchi, D. Arp, and K. Rieck, “Pulsar: Stateful black-box fuzzing of proprietary network protocols,” in Security and Privacy in Communication Networks: 11th EAI Interna- tional Conference, SecureComm 2015, Dallas, TX, USA, October 26-29, 2015, Proceedings 11 . Springer, 2015, pp. 330–347

    work page 2015

  28. [28]

    American fuzzy lop,

    M. Zalewski, “American fuzzy lop,” 2014, http://lcamtuf.coredump.cx/ afl/

    work page 2014

  29. [29]

    {AFL++}: Combin- ing incremental steps of fuzzing research,

    A. Fioraldi, D. Maier, H. Eißfeldt, and M. Heuse, “ {AFL++}: Combin- ing incremental steps of fuzzing research,” in 14th USENIX Workshop on Offensive Technologies (WOOT 20) , 2020

    work page 2020

  30. [30]

    Aflnet: a greybox fuzzer for network protocols,

    V .-T. Pham, M. B¨ohme, and A. Roychoudhury, “Aflnet: a greybox fuzzer for network protocols,” in 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) . IEEE, 2020, pp. 460–465

    work page 2020

  31. [31]

    Nsfuzz: Towards efficient and state-aware network service fuzzing,

    S. Qin, F. Hu, Z. Ma, B. Zhao, T. Yin, and C. Zhang, “Nsfuzz: Towards efficient and state-aware network service fuzzing,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 6, pp. 1–26, 2023

    work page 2023

  32. [32]

    Augmenting greybox fuzzing with generative ai,

    J. Hu, Q. Zhang, and H. Yin, “Augmenting greybox fuzzing with generative ai,” arXiv preprint arXiv:2306.06782 , 2023

    Pith/arXiv arXiv 2023

  33. [33]

    Msfuzz: Augmenting protocol fuzzing with message syntax comprehension via large language models

    M. Cheng, K. Zhu, Y . Chen, G. Yang, Y . Lu, and C. Lu, “Msfuzz: Augmenting protocol fuzzing with message syntax comprehension via large language models.” Electronics (2079-9292), vol. 13, no. 13, 2024

    work page 2079

  34. [34]

    Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,

    Y . Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang, “Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,” in Proceedings of the 32nd ACM SIGSOFT interna- tional symposium on software testing and analysis , 2023, pp. 423–435

    2023

  35. [35]

    Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt,

    Y . Deng, C. S. Xia, C. Yang, S. D. Zhang, S. Yang, and L. Zhang, “Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt,” arXiv preprint arXiv:2304.02014 , 2023

    Pith/arXiv arXiv 2023

  36. [36]

    NVD - Home,

    “NVD - Home,” https://nvd.nist.gov/, [Accessed 19-06-2025]

    work page 2025

  37. [37]

    LangChain,

    “LangChain,” https://www.langchain.com/, [Accessed 27-05-2025]

    work page 2025

  38. [38]

    “CrewAI,” https://www.crewai.com/, [Accessed 27-05-2025]

    work page 2025

  39. [39]

    Groq is Fast AI Inference,

    “Groq is Fast AI Inference,” https://groq.com/, [Accessed 19-06-2025]

    work page 2025

  40. [40]

    Profuzzbench: A benchmark for stateful protocol fuzzing,

    R. Natella and V .-T. Pham, “Profuzzbench: A benchmark for stateful protocol fuzzing,” in Proceedings of the 30th ACM SIGSOFT interna- tional symposium on software testing and analysis , 2021, pp. 662–665

    work page 2021

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