Pith. sign in

REVIEW 3 major objections 5 minor 178 references

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper argues that an orchestrated set of large-language-model agents, called ReManGPT, can carry knowledge, plans, and records across remanufacturing stages, reducing the need for specialized human expertise and moving the industry…

desk verdict Honest, well-scoped conceptual framework for LLM-based remanufacturing; the validation gap in shared process memory is real but mostly a matter of elaboration, not a fatal flaw. read the letter →

arxiv 2608.04854 v1 pith:TAW4NDNF submitted 2026-08-05 eess.SY cs.SY

Chang Liu , Sara Behdad , Prabhakar Pagilla , Xiao Liang , Minghui Zheng This is my paper
classification eess.SYcs.SY
keywords largelanguagemodelsremanufacturingcirculareconomyagenticframeworkdisassemblyplanningvision-language-actionhuman-robotcollaborationretrieval-augmentedgeneration
verification ladder T0 review T1 audit T2 compute T3 formal

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 argues that remanufacturing, which depends on skilled human judgment because end-of-life products arrive in unpredictable condition, can move toward higher automation if large language models are organized into a coordinating framework. It introduces ReManGPT, a conceptual architecture in which an orchestration layer routes knowledge retrieval, reasoning, planning, and execution among specialized LLM agents while a shared process memory carries each product's history across stages. The paper's review of sixty LLM-related studies shows that current work is fragmented, and its three case studies test planning, repair guidance, and robotic action modules separately. If the framework works as intended, operators without programming expertise could retrieve context-aware instructions, revise plans in natural language, and hand validated plans to robots, reducing dependence on senior experts.

What carries the argument

The central object is ReManGPT, an agentic framework defined by a central orchestration layer that coordinates four elements: a remanufacturing knowledge foundation (curated manuals, images, CAD data, and records), shared process memory (case-specific product-component histories), LLM-enabled functional agents, and operational interfaces (operator, robotic, machine, and process-planning outputs). The mechanism that carries the argument is the closed-loop workflow: task requests are interpreted with current memory context, agents retrieve evidence and invoke tools under harness-engineering constraints, outputs are checked and routed to an interface, and execution results, deviations, and feedback are written back to memory for downstream stages. This turns scattered LLM outputs into traceable, stage-linked records that can support later decisions and design-for-remanufacturing feedback.

What would settle it

Run a full ReManGPT-style pipeline on a batch of end-of-life desktops with known ground-truth disassembly plans and safe, verified operation sequences, with no human correction during execution; if any unsafe or infeasible instruction is emitted and passes the framework's own validation checks, the central claim of reliable grounding fails for that setting.

Watch

Extended reading notes

Core claim

The central claim is that the missing piece in remanufacturing automation is not a single model but a coordination layer. ReManGPT provides that layer by connecting a curated knowledge foundation, LLM-enabled functional agents (assessment, retrieval, planning, repair guidance, human interaction, robotic execution, memory update, and others), and operational interfaces through a central orchestration layer, with harness engineering constraining each agent's role, evidence grounding, safety rules, and output format. The framework treats remanufacturing as a chain of interdependent decisions: inspection constrains disassembly, disassembly refines component state, and records carry forward into repair, reassembly, and testing. The paper's evidence includes a disassembly-planning module that turns an image and a query into a sequenced plan with tool requirements, a repair-guidance module that turns failure symptoms into diagnostic steps and a battery-replacement procedure, and a vision-language-action-based execution module that predicts robot actions but does not yet complete contact-rich removal. The paper presents ReManGPT as a modular framework for integrating existing capabilities, not as a finished product.

Load-bearing premise

The load-bearing premise is that language-model outputs can be made reliably grounded, safe, and verified through retrieval, knowledge grounding, and harness engineering; if hallucinated instructions slip through, the orchestration layer would spread faulty plans to operators and robots.

Editorial extensions

If this is right

  • Operators without programming expertise could generate and revise disassembly and repair plans through natural language, lowering the skill barrier at the shop floor.
  • A validated plan from a planning module could be routed directly to a robotic execution module, forming a closed perception-to-action loop.
  • Shared process memory would give each end-of-life product a traceable component history, improving reassembly, testing, and quality verification decisions.
  • Accumulated operational records could feed process planning and design-for-remanufacturing, revealing recurring features that hinder inspection, disassembly, and repair.
  • The same framework could be applied to electric vehicle batteries, electronic waste, and electric motors, where uncertainty currently forces manual expert handling.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the orchestration-plus-memory architecture could be tested on a domain with standardized ground truth, such as phone or laptop refurbishment, to quantify how much operator time and error the framework actually saves.
  • The paper's vision-language-action case suggests a testable research prediction: adding force-tactile sensing and historical state input to an execution module would close the gap between approaching a component and reliably removing it after contact.
  • If ReManGPT validation records became a shared standard, they could double as training data for future models, turning decision support into a compounding data asset.
  • A further consequence the paper leaves implicit: the framework's largest value may lie in capturing senior workers' institutional knowledge into retrievable records before that expertise leaves the workforce, rather than in full automation.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents a forward-looking review of LLM applications in remanufacturing and proposes ReManGPT, an agentic framework comprising a central orchestration layer, shared process memory, LLM-enabled functional agents, a knowledge foundation, and operational interfaces. It includes three case studies (disassembly sequence planning, laptop repair guidance, and VLA-based robotic disassembly) as illustrative module instantiations, and discusses three applications (EV batteries, e-waste, electric motors) plus limitations and future directions. The paper explicitly states that ReManGPT is a conceptual framework, not a complete solution, and that hallucination and validation remain open problems.

Significance. The paper addresses a genuine gap: prior LLM work in remanufacturing is task-specific and fragmented, and no coherent framework exists for cross-stage coordination. The proposed architecture, if validated, could reduce reliance on scarce expert labor and improve adaptability under end-of-life uncertainty. The authors are honest about limitations, explicitly scoping the contribution as conceptual and providing three concrete demonstrations with collected data (over 2,800 fine-tuning pairs for disassembly planning and 288 teleoperation sets for VLA). The forward-looking analysis of EV batteries, e-waste, and electric motors is useful. However, the central claim is not yet supported by integrated evidence; the paper is best read as a framework proposal and research agenda rather than a validated system.

major comments (3)
  1. [Section III.A.2–III.A.4, Fig. 6] Shared process memory is described as the mechanism that carries validated records across stages, but the workflow does not specify an independent validation gate that must be passed before a record (e.g., an inspection result, disassembly task-plan record, or repair treatment record) is written to memory and reused by downstream agents. Section V concedes that hallucination 'remains a major barrier' and can cause 'direct and severe failures' in technical operations. In a multi-stage pipeline, a single erroneous record could propagate through inspection, disassembly, repair, and reassembly, undermining the framework's central promise of 'traceable product-component histories.' The authors should either specify the validation procedure (e.g., a verification agent, cross-check against sensor data, or mandatory human sign-off) or explicitly restrict the memory-update claim to records that have undergone such validation. As written, the architecture's reliability claim is not established.
  2. [Section III.B, Table I] The three case studies are evaluated as isolated module-level instantiations, and none exercises the orchestration layer or shared process memory across more than one remanufacturing stage. The paper's central claim in Section III.A.1 is that remanufacturing requires a coordination mechanism that transfers validated records across stages; however, the demonstrations do not show any cross-stage transfer, memory reuse, or orchestrated replanning. The authors state in Section III.A.5 that 'the demonstrations are evaluated separately,' which confirms the absence of integration testing. At least one integrated scenario (e.g., a disassembly-planning record routed to the robotic-execution module, with the execution outcome written back to memory) would be needed to support the claimed coordinating benefit; without it, the added value of ReManGPT over the sum of its parts remains an assertion.
  3. [Section III.B.1, III.B.3, Section VII] The case studies, while honestly reported, show that the current modules fall short of the capability required to reduce reliance on human expertise: the disassembly-planning module fails on unfamiliar desktop layouts, the repair module depends on knowledge-base coverage, and the VLA-based robotic execution module 'does not complete the full pick-and-disassemble sequence.' The paper acknowledges these shortfalls in Section VII, but the central claim in Section III.A (that ReManGPT can 'support knowledge retrieval, reasoning, planning, and execution across major remanufacturing stages and thereby reduce reliance on human expertise') is not backed by these results. The authors should either reframe the central claim as a research agenda with clearly stated feasibility conditions, or add the missing reliability mechanisms (e.g., human-in-the-loop verification, force/tactile sensing, more comprehensive knowledge coverage) to the framework description.
minor comments (5)
  1. [Section II.C] The sentence 'It may also introduce inconsistency in output quality [96]' is duplicated in the same paragraph; one occurrence should be removed.
  2. [Figure 4] The search window is given as 2019–2026, but the paper includes 2026 references (e.g., [44], [53], [56]) that are presumably preprints or early-access items; the authors should state the exact screening date and clarify how '2026' items were handled.
  3. [Section III.A.2] The capitalization of 'Harness Engineering' is inconsistent with 'harness engineering' elsewhere; please unify.
  4. [Table I] The 'Generated record' column for the VLA-based robotic disassembly case states 'Predicted robot actions and execution outcomes' but does not mention whether the outcome record is validated before storage; adding the validation status would align with the shared-memory discussion.
  5. [Section IV] The market statistics for the three applications are reported with different years and sources; a table or consistent citation year would improve comparability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: ReManGPT is a conceptual framework with illustrative case studies, not a fitted prediction or derived quantitative result.

full rationale

The paper makes no quantitative predictions and fits no parameters. ReManGPT is explicitly introduced as a conceptual framework, and the three case studies are framed as module-level illustrations rather than as validation of the central claim. The workflow descriptions connect architectural elements without deriving any output from the framework itself. Author self-citations appear in the literature review, case-study background, and future directions, but none is invoked as the sole justification for the framework's ability to coordinate remanufacturing stages. The paper repeatedly disclaims that full automation has not been achieved, that the VLA case does not complete the pick-and-disassemble sequence, and that hallucination remains a major barrier. No load-bearing reduction of a claimed result to its own inputs is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper introduces a conceptual framework rather than a quantitative model. It relies on domain assumptions about LLM capability, the feasibility of grounding and knowledge structuring, and the representativeness of its literature sample. No free parameters are fitted. The only invented entity is the ReManGPT framework itself, which currently lacks independent evidence.

assumptions (4)
  • domain assumption LLMs possess the capabilities (reasoning, planning, knowledge retrieval, natural-language interpretation) required to support remanufacturing tasks.
    The entire framework relies on this; the paper cites general LLM capabilities but does not validate them on remanufacturing tasks beyond small illustrative cases. See Sections I and II.
  • domain assumption The orchestration layer and harness engineering can constrain LLM outputs to be safe, evidence-grounded, and operationally usable.
    This is the design premise of ReManGPT (Section III-A-2), but no end-to-end safety or reliability evaluation is provided; Section V itself notes hallucination remains a major barrier.
  • domain assumption Remanufacturing knowledge can be collected, structured, and maintained in a reusable form (knowledge foundation) for retrieval and fine-tuning.
    The framework assumes this is feasible; Section V acknowledges significant data limitations.
  • domain assumption The 60 studies selected through the Google Scholar search are representative of the state of the art.
    The review's coverage depends on this; the screening criteria are not fully specified (Section II, Fig. 4).
invented entities (1)
  • ReManGPT
    purpose: Conceptual agentic framework coordinating LLM-enabled agents, shared process memory, and operational interfaces for remanufacturing.
    Introduced in this paper as a conceptual framework; no implementation or external validation is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies." pith.science (2026). https://pith.science/paper/TAW4NDNF

@misc{pith2026260804854,
  author       = {Pith},
  title        = {Pith review of: Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TAW4NDNF}},
  note         = {Machine review of arXiv:2608.04854}
}
read the original abstract

With growing concerns about resource scarcity and environmental degradation, remanufacturing of end-of-life (EoL) products within the circular economy is attracting increasing attention. Remanufacturing can preserve most of the original manufacturing value and materials while transforming EoL products into like-new condition. However, the variability and uncertainty of EoL products make remanufacturing highly dependent on human expertise. Recently, large language models (LLMs) have demonstrated remarkable capabilities in learning from massive, unstructured datasets, generating expert-level output across various tasks, and communicating with humans in natural language for interpretation. These advantages can align closely with the complex demands of remanufacturing, thereby mitigating the reliance on specialized expertise. However, their roles and research progress in this domain remain underexplored. In this paper, we present a forward-looking review and analysis of the role of LLMs in remanufacturing automation, grounded in a brief critical review of existing LLM-related studies relevant to remanufacturing. Building on this foundation, we introduce ReManGPT as a conceptual framework and use three representative case studies to illustrate selected modules of the framework in practical remanufacturing scenarios. We also analyze three representative remanufacturing applications, electric vehicle batteries, electronic waste, and electric motors, to illustrate how the proposed framework could address their domain-specific challenges. Finally, we discuss the current barriers to deploying this framework in practice and outline future research directions, including LLM-assisted human operation and language-action models for robotic automation.

Figures

Figures reproduced from arXiv: 2608.04854 by the authors.

Figure 1
Figure 1. The comparison between the linear and circular [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Evolution of remanufacturing automation from traditional manual operation to human-centric automation and future [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Motivation for introducing ReManGPT in LLM-enabled intelligent remanufacturing. The left side shows heterogeneous [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Search and screening process for identifying representative LLM-related remanufacturing studies used in the brief [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Conceptual architecture and information flow of ReManGPT for remanufacturing. Heterogeneous remanufacturing [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: ReManGPT-enabled remanufacturing workflow with knowledge retrieval, operational interface routing, and shared [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Case study of the ReManGPT disassembly-planning module using a multi-modal LLM and KG-based knowledge [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Case study of the ReManGPT repair-guidance module using LLM and RAG-based repair-guide retrieval. The module [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Case study of the ReManGPT robotic-execution module for VLA-based EoL desktop disassembly. The module converts [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

178 extracted references · 55 canonical work pages

  1. [1]

    The circular economy–a new sustainability paradigm?

    M. Geissdoerfer, P. Savaget, N. M. Bocken, and E. J. Hultink, “The circular economy–a new sustainability paradigm?”Journal of cleaner production, vol. 143, pp. 757–768, 2017

    2017

  2. [2]

    Strategies for manufacturing,

    R. A. Frosch and N. E. Gallopoulos, “Strategies for manufacturing,”Scientific American, vol. 261, no. 3, pp. 144–153, 1989

    1989

  3. [3]

    Unlocking value for a circular economy through 3d printing: A research agenda,

    M. Despeisse, M. Baumers, P. Brown, F. Charnley, S. J. Ford, A. Garmulewicz, S. Knowles, T. Minshall, L. Mortara, F. Reed-Tsochaset al., “Unlocking value for a circular economy through 3d printing: A research agenda,”Technological Forecasting and Social Change, vol. 115, pp. 75–84, 2017

    2017

  4. [4]

    A review on remanufacturing, reuse, and recycling in supply chain—exploring the evolution of information tech- nology over two decades,

    N. M. Modak, S. Sinha, and D. K. Ghosh, “A review on remanufacturing, reuse, and recycling in supply chain—exploring the evolution of information tech- nology over two decades,”International Journal of Information Management Data Insights, vol. 3, no. 1, p. 100160, 2023

    2023

  5. [5]

    Remanufac- turing for the circular economy: Study and evaluation of critical factors,

    D. Singhal, S. Tripathy, and S. K. Jena, “Remanufac- turing for the circular economy: Study and evaluation of critical factors,”Resources, Conservation and Recy- cling, vol. 156, p. 104681, 2020

    2020

  6. [6]

    Determinants of consumer demand for circular economy products. a case for reuse and remanufacturing for sustainable development,

    A. D. Hunka, M. Linder, and S. Habibi, “Determinants of consumer demand for circular economy products. a case for reuse and remanufacturing for sustainable development,”Business Strategy and the Environment, vol. 30, no. 1, pp. 535–550, 2021

    2021

  7. [7]

    Effective adoption of remanufacturing practices: a step towards circular economy,

    S. Khan, A. Haleem, and N. Fatma, “Effective adoption of remanufacturing practices: a step towards circular economy,”Journal of Remanufacturing, vol. 12, no. 2, pp. 167–185, 2022

    2022

  8. [8]

    Second life use of li-ion batteries in the heavy-duty vehicle industry: Feasibilities of remanufacturing, repurposing, and reusing approaches,

    K. Chirumalla, I. Kulkov, F. Vu, and M. Rahic, “Second life use of li-ion batteries in the heavy-duty vehicle industry: Feasibilities of remanufacturing, repurposing, and reusing approaches,”Sustainable Production and consumption, vol. 42, pp. 351–366, 2023

    2023

Show all 178 references
  1. [9]

    Reducing waste: repair, recondition, re- manufacture or recycle?

    A. M. King, S. C. Burgess, W. Ijomah, and C. A. McMahon, “Reducing waste: repair, recondition, re- manufacture or recycle?”Sustainable development, vol. 14, no. 4, pp. 257–267, 2006

    2006

  2. [10]

    A review of the state of the art in tools and techniques used to eval- uate remanufacturing feasibility,

    P. Goodall, E. Rosamond, and J. Harding, “A review of the state of the art in tools and techniques used to eval- uate remanufacturing feasibility,”Journal of Cleaner Production, vol. 81, pp. 1–15, 2014

    2014

  3. [11]

    Sustainable remanufacturing management approaches and applications model in end-of-life vehicles: a critical review and classification,

    J. Y . Chong, M. Z. Mat Saman, and N. H. A. Ngadiman, “Sustainable remanufacturing management approaches and applications model in end-of-life vehicles: a critical review and classification,”Journal of Remanufacturing, vol. 14, no. 1, pp. 169–184, 2024

    2024

  4. [12]

    Do fairness concerns matter for esg decision- making? strategic interactions in digital twin-enabled sustainable semiconductor supply chain,

    M. Zhang, W. Yang, Z. Zhao, S. Wang, and G. Q. Huang, “Do fairness concerns matter for esg decision- making? strategic interactions in digital twin-enabled sustainable semiconductor supply chain,”International Journal of Production Economics, vol. 276, p. 109370, 2024

    2024

  5. [13]

    How does green manufacturing enhance corporate esg perfor- mance?—empirical evidence from machine learning and text analysis,

    H. Zeng, C. Yu, and G. Zhang, “How does green manufacturing enhance corporate esg perfor- mance?—empirical evidence from machine learning and text analysis,”Journal of Environmental Manage- ment, vol. 370, p. 122933, 2024

    2024

  6. [14]

    Remanufacture for sustainability: a comprehen- sive business model for automotive parts remanufactur- ing,

    H. N. Gunasekara, J. R. Gamage, and H. K. Punchi- hewa, “Remanufacture for sustainability: a comprehen- sive business model for automotive parts remanufactur- ing,”International journal of sustainable engineering, vol. 14, no. 6, pp. 1386–1395, 2021

    2021

  7. [15]

    Reliability and cost optimization for re- manufacturing process planning,

    Z. Jiang, T. Zhou, H. Zhang, Y . Wang, H. Cao, and G. Tian, “Reliability and cost optimization for re- manufacturing process planning,”Journal of cleaner production, vol. 135, pp. 1602–1610, 2016

    2016

  8. [16]

    Instilling lifecycle costs into modular product development for improved remanufacturing-product service system enterprise,

    J. A. Fadeyi and L. Monplaisir, “Instilling lifecycle costs into modular product development for improved remanufacturing-product service system enterprise,”In- ternational Journal of Production Economics, vol. 246, p. 108404, 2022

    2022

  9. [17]

    A review on the lifecycle strategies enhancing remanufacturing,

    R. F. Fofou, Z. Jiang, and Y . Wang, “A review on the lifecycle strategies enhancing remanufacturing,”Ap- plied Sciences, vol. 11, no. 13, p. 5937, 2021

    2021

  10. [18]

    M. A. Ilgin and S. M. Gupta,Remanufacturing model- ing and analysis. CRC Press, 2012

    2012

  11. [19]

    Reman- ufacturing for the circular economy: An examination of consumer switching behavior,

    B. T. Hazen, D. A. Mollenkopf, and Y . Wang, “Reman- ufacturing for the circular economy: An examination of consumer switching behavior,”Business Strategy and the Environment, vol. 26, no. 4, pp. 451–464, 2017

    2017

  12. [20]

    Value-retained vs. impacts avoided: the differentiated contributions of remanufac- turing, refurbishment, repair, and reuse within a circular economy,

    J. D. Russell and N. Z. Nasr, “Value-retained vs. impacts avoided: the differentiated contributions of remanufac- turing, refurbishment, repair, and reuse within a circular economy,”Journal of Remanufacturing, vol. 13, no. 1, pp. 25–51, 2023

    2023

  13. [21]

    How to select remanufac- turing mode: end-of-life or used product?

    S. Wu, J. Cao, and Q. Shao, “How to select remanufac- turing mode: end-of-life or used product?”Environment, Development and Sustainability, vol. 27, no. 6, pp. 14 175–14 195, 2025

    2025

  14. [22]

    Remanufacturing electric vehicle battery supply chain under government subsi- dies and carbon trading: Optimal pricing and return policy,

    Y .-C. Tsao and H. T. T. Ai, “Remanufacturing electric vehicle battery supply chain under government subsi- dies and carbon trading: Optimal pricing and return policy,”Applied Energy, vol. 375, p. 124063, 2024

    2024

  15. [23]

    Disassem- bly analysis to promote rare earth permanent magnet recovery from end-of-life electric vehicle motors,

    T. Maani, S. Deng, and J. W. Sutherland, “Disassem- bly analysis to promote rare earth permanent magnet recovery from end-of-life electric vehicle motors,” in International Manufacturing Science and Engineering Conference, vol. 88100. American Society of Mechan- ical Engineers...

    2024

  16. [24]

    Regional rare- earth element supply and demand balanced with circular economy strategies,

    P. Wang, Y .-Y . Yang, O. Heidrich, L.-Y . Chen, L.-H. Chen, T. Fishman, and W.-Q. Chen, “Regional rare- earth element supply and demand balanced with circular economy strategies,”Nature Geoscience, vol. 17, no. 1, pp. 94–102, 2024

    2024

  17. [25]

    The dynamics of accelerating end-of-life rare earth permanent magnet recycling: A technological innova- tion systems approach,

    M. Koese, S. van Nielen, J. Bradley, and R. Kleijn, “The dynamics of accelerating end-of-life rare earth permanent magnet recycling: A technological innova- tion systems approach,”Applied Energy, vol. 388, p. 125707, 2025

    2025

  18. [26]

    Trends and research challenges in remanufacturing,

    M. Matsumoto, S. Yang, K. Martinsen, and Y . Kainuma, “Trends and research challenges in remanufacturing,” International journal of precision engineering and manufacturing-green technology, vol. 3, pp. 129–142, 2016

    2016

  19. [27]

    Industrial machinery remanufacturing market: Size, share, trends, growth outlook and opportunities to 2034,

    U. Analytics, “Industrial machinery remanufacturing market: Size, share, trends, growth outlook and opportunities to 2034,” 2025. [Online]. Avail- able: https://www.usdanalytics.com/industry-reports/ industrial-machinery-remanufacturing-market

    2025

  20. [28]

    A multi- objective integrated scheduling of remanufacturing sys- tem considering time window constrained outsourcing option,

    J. Guo, W. Guo, B. Du, J. Zou, and K. Wang, “A multi- objective integrated scheduling of remanufacturing sys- tem considering time window constrained outsourcing option,”Journal of Cleaner Production, vol. 468, p. 142916, 2024

    2024

  21. [29]

    Diagnosing remanufacture potential at product-component level: A disassembla- bility and integrity approach,

    L. Sierra-Fontalvo, J. Polo-Cardozo, H. Maury- Ram´ırez, and J. A. Mesa, “Diagnosing remanufacture potential at product-component level: A disassembla- bility and integrity approach,”Resources, Conservation and Recycling, vol. 205, p. 107529, 2024

    2024

  22. [30]

    Achieving remanufacturing inspection us- ing deep learning,

    C. Nwankpa, S. Eze, W. Ijomah, A. Gachagan, and S. Marshall, “Achieving remanufacturing inspection us- ing deep learning,”Journal of Remanufacturing, vol. 11, no. 2, pp. 89–105, 2021

    2021

  23. [31]

    Enhancing remanufacturing operations: A review on decision-making models and their implementation challenges,

    M. Caterino, R. Iannone, R. Macchiaroli, S. Riemma, D. T. Pham, and M. Fera, “Enhancing remanufacturing operations: A review on decision-making models and their implementation challenges,”Computers & Indus- trial Engineering, p. 111088, 2025

    2025

  24. [32]

    A systematic review of decision-making in remanufacturing,

    M. I. Rizova, T. Wong, and W. Ijomah, “A systematic review of decision-making in remanufacturing,”Com- puters & Industrial Engineering, vol. 147, p. 106681, 2020

    2020

  25. [33]

    Remanufacturing challenges and possible lean im- provements,

    J. Kurilova-Palisaitiene, E. Sundin, and B. Poksinska, “Remanufacturing challenges and possible lean im- provements,”Journal of Cleaner Production, vol. 172, pp. 3225–3236, 2018

    2018

  26. [34]

    Re- view on current challenges and future opportunities in malaysia sustainable manufacturing: Remanufacturing industries,

    H. J. Ngu, M. D. Lee, and M. S. B. Osman, “Re- view on current challenges and future opportunities in malaysia sustainable manufacturing: Remanufacturing industries,”Journal of Cleaner Production, vol. 273, p. 123071, 2020

    2020

  27. [35]

    Product reuse and repurpose in circular manufacturing: a critical review of key challenges, shortcomings and future di- rections,

    F. Psarommatis, G. May, and V . Azamfirei, “Product reuse and repurpose in circular manufacturing: a critical review of key challenges, shortcomings and future di- rections,”Journal of Remanufacturing, pp. 1–38, 2025

    2025

  28. [36]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”Advances in neural infor- mation processing systems, vol. 30, 2017

    2017

  29. [37]

    Unleash- ing the potential of prompt engineering in large lan- guage models: a comprehensive review,

    B. Chen, Z. Zhang, N. Langren ´e, and S. Zhu, “Unleash- ing the potential of prompt engineering in large lan- guage models: a comprehensive review,”arXiv preprint arXiv:2310.14735, 2023

    2023 arXiv

  30. [38]

    E- waste challenges of generative artificial intelligence,

    P. Wang, L.-Y . Zhang, A. Tzachor, and W.-Q. Chen, “E- waste challenges of generative artificial intelligence,” Nature Computational Science, pp. 1–6, 2024

    2024

  31. [39]

    Towards green ai: Current status and future re- search,

    C. Clemm, L. Stobbe, K. Wimalawarne, and J. Dr- uschke, “Towards green ai: Current status and future re- search,” in2024 Electronics Goes Green 2024+(EGG). IEEE, 2024, pp. 1–11

    2024

  32. [40]

    Fine-tuning and utilization methods of domain-specific llms,

    C. Jeong, “Fine-tuning and utilization methods of domain-specific llms,”arXiv preprint arXiv:2401.02981, 2024

    2024 arXiv

  33. [41]

    Large lan- guage models in medicine,

    A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, “Large lan- guage models in medicine,”Nature medicine, vol. 29, no. 8, pp. 1930–1940, 2023

    1930

  34. [42]

    Leveraging large language models for pre- dictive chemistry,

    K. M. Jablonka, P. Schwaller, A. Ortega-Guerrero, and B. Smit, “Leveraging large language models for pre- dictive chemistry,”Nature Machine Intelligence, vol. 6, no. 2, pp. 161–169, 2024

    2024

  35. [43]

    Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities,

    C. Chen, K. Zhao, J. Leng, C. Liu, J. Fan, and P. Zheng, “Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities,”Robotics and Computer-Integrated Manufacturing, vol. 94, p. 102982, 2025

    2025

  36. [44]

    Ccm-fcc: Llm-powered cognition-centered ai agent framework for proactive human-robot collaboration,

    P. Ding, J. Zhang, P. Zhang, H. Li, and D. Wang, “Ccm-fcc: Llm-powered cognition-centered ai agent framework for proactive human-robot collaboration,” Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103145, 2026

    2026

  37. [45]

    Afp automated inspection system per- formance and expectations,

    J. Cemenska, T. Rudberg, M. Henscheid, A. Lauletta, and B. Davis, “Afp automated inspection system per- formance and expectations,” 2017

    2017

  38. [46]

    An inspection and classification system for automotive component remanufacturing industry based on ensemble learning,

    F. A. Saiz, G. Alfaro, and I. Barandiaran, “An inspection and classification system for automotive component remanufacturing industry based on ensemble learning,” Information, vol. 12, no. 12, p. 489, 2021

    2021

  39. [47]

    A remanufacturing cost prediction model of used parts considering failure characteristics,

    X. Zhang, X. Ao, Z. Jiang, H. Zhang, and W. Cai, “A remanufacturing cost prediction model of used parts considering failure characteristics,”Robotics and Computer-Integrated Manufacturing, vol. 59, pp. 291– 296, 2019

    2019

  40. [48]

    Ai-enhanced iden- tification, inspection and sorting for reverse logistics in remanufacturing,

    M. Schl ¨uter, H. Lickert, K. Schweitzer, P. Bilge, C. Briese, F. Dietrich, and J. Kr¨uger, “Ai-enhanced iden- tification, inspection and sorting for reverse logistics in remanufacturing,”Procedia CIRP, vol. 98, pp. 300–305, 2021

    2021

  41. [49]

    Optimizing e-waste management: Deep learning classifiers for effective planning,

    S. Selvakanmani, P. Rajeswari, B. Krishna, and J. Manikandan, “Optimizing e-waste management: Deep learning classifiers for effective planning,”Journal of Cleaner Production, vol. 443, p. 141021, 2024

    2024

  42. [50]

    Vision guided robotic inspection for parts in manufacturing and remanufacturing industry,

    A. Khan, C. Mineo, G. Dobie, C. Macleod, and G. Pierce, “Vision guided robotic inspection for parts in manufacturing and remanufacturing industry,”Journal of Remanufacturing, vol. 11, no. 1, pp. 49–70, 2021

    2021

  43. [51]

    A concept for autonomous quality control for core inspec- tion in remanufacturing,

    J.-P. Kaiser, S. Lang, M. Wurster, and G. Lanza, “A concept for autonomous quality control for core inspec- tion in remanufacturing,”Procedia CIRP, vol. 105, pp. 374–379, 2022

    2022

  44. [52]

    Design for automated inspection in remanufacturing: A discrete event simulation for process improvement,

    C. E. Nwankpa, W. Ijomah, and A. Gachagan, “Design for automated inspection in remanufacturing: A discrete event simulation for process improvement,”Cleaner Engineering and Technology, vol. 4, p. 100199, 2021

    2021

  45. [53]

    Human–robot collaborative visual inspection with large language models,

    O. Tasneem and R. Pieters, “Human–robot collaborative visual inspection with large language models,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103154, 2026

    2026

  46. [54]

    Disassembly sequence planning of equipment decommissioning for industry 5.0: Prospects and retrospects,

    L. He, J. Gao, J. Leng, Y . Wu, K. Ding, L. Ma, J. Liu, and D. T. Pham, “Disassembly sequence planning of equipment decommissioning for industry 5.0: Prospects and retrospects,”Advanced Engineering Informatics, vol. 62, p. 102939, 2024

    2024

  47. [55]

    Empowering natural human– robot collaboration through multimodal language mod- els and spatial intelligence: Pathways and perspec- tives,

    D. Wu, P. Zheng, Q. Zhao, S. Zhang, J. Qi, J. Hu, G.- N. Zhu, and L. Wang, “Empowering natural human– robot collaboration through multimodal language mod- els and spatial intelligence: Pathways and perspec- tives,”Robotics and Computer-Integrated Manufactur- ing, vol. 97, p. 1...

    2026

  48. [56]

    Batch eol products human-robot collabora- tive remanufacturing process planning and scheduling for industry 5.0,

    Y . Liu, G. Tian, H. Sheng, X. Zhang, G. Yuan, and C. Zhang, “Batch eol products human-robot collabora- tive remanufacturing process planning and scheduling for industry 5.0,”Robotics and Computer-Integrated Manufacturing, vol. 97, p. 103098, 2026

    2026

  49. [57]

    Raise: A robot-assisted selective disas- sembly and sorting system for end-of-life phones,

    C. Liu, B. Balasubramaniam, N. A. Yancey, M. H. Severson, A. Shine, P. Bove, B. Li, X. Liang, and M. Zheng, “Raise: A robot-assisted selective disas- sembly and sorting system for end-of-life phones,” Resources, Conservation and Recycling, vol. 225, p. 108609, 2026

    2026

  50. [58]

    Task allocation and planning for product disassembly with human–robot collaboration,

    M.-L. Lee, S. Behdad, X. Liang, and M. Zheng, “Task allocation and planning for product disassembly with human–robot collaboration,”Robotics and Computer- Integrated Manufacturing, vol. 76, p. 102306, 2022

    2022

  51. [59]

    A human- cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in industry 5.0,

    S. Lou, Y . Zhang, R. Tan, and C. Lv, “A human- cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in industry 5.0,”Robotics and Computer-Integrated Manufacturing, vol. 87, p. 102706, 2024

    2024

  52. [60]

    Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot col- laborative disassembly: A case of retired electric vehicle battery,

    J. Gao, G. Wang, J. Xiao, P. Zheng, and E. Pei, “Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot col- laborative disassembly: A case of retired electric vehicle battery,”Robotics and Computer-Integrated Manufac- turing, vol...

    2024

  53. [61]

    En- hancing remanufacturing efficiency: a genetic teaching- learning-based optimisation algorithm for human-robot shared-workstation disassembly line balancing prob- lem,

    Q. Duan, Z. Zhang, J. Hu, L. Guo, and W. Liang, “En- hancing remanufacturing efficiency: a genetic teaching- learning-based optimisation algorithm for human-robot shared-workstation disassembly line balancing prob- lem,”Robotics and Computer-Integrated Manufactur- ing, vol. 97...

    2026

  54. [62]

    A green and efficient disassembly line balancing with human-robot collaboration and destructive disassem- bly,

    W. Zhang, Y . Li, K. Wang, W. Xu, and L. Gao, “A green and efficient disassembly line balancing with human-robot collaboration and destructive disassem- bly,”Robotics and Computer-Integrated Manufacturing, vol. 97, p. 103081, 2026

    2026

  55. [63]

    Per- sonalized disassembly sequence planning for a human– robot hybrid disassembly cell,

    S. Lou, R. Tan, Y . Zhang, M. Zhou, and C. Lv, “Per- sonalized disassembly sequence planning for a human– robot hybrid disassembly cell,”IEEE Transactions on Industrial Informatics, vol. 20, no. 9, pp. 11 372–11 383, 2024

    2024

  56. [64]

    A fuzzy knowledge-based disassembly pro- cess planning system based on fuzzy attributed and timed predicate/transition net,

    H.-P. Hsu, “A fuzzy knowledge-based disassembly pro- cess planning system based on fuzzy attributed and timed predicate/transition net,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 1800–1813, 2016

    2016

  57. [65]

    A study on the predictive capabilities of digital twins for object transfers in a remanufacturing demonstration environment,

    J.-F. Klein and K. Furmans, “A study on the predictive capabilities of digital twins for object transfers in a remanufacturing demonstration environment,”Robotics and Computer-Integrated Manufacturing, vol. 97, p. 103063, 2026

    2026

  58. [66]

    Review and perspectives on multimodal per- ception, mutual cognition, and embodied execution for human–robot collaboration in industry 5.0,

    K. Ding, Q. Mao, Y . Zhang, Y . Zhang, P. Zheng, and L. Wang, “Review and perspectives on multimodal per- ception, mutual cognition, and embodied execution for human–robot collaboration in industry 5.0,”Robotics and Computer-Integrated Manufacturing, vol. 101, p. 103280, 2026

    2026

  59. [67]

    A distributed con- trol architecture for a multi-agent robotic cell: a battery pack disassembly case study,

    M. Ferrari, R. Fausti, C. Tonola, M. Delledonne, S. San- drini, M. Beschi, and E. Villagrossi, “A distributed con- trol architecture for a multi-agent robotic cell: a battery pack disassembly case study,”International Journal of Computer Integrated Manufacturing, pp. 1–21, 2025

    2025

  60. [68]

    Robotic disassembly sequence dynamic planning un- der uncertain irremovable condition using dueling deep q-network based on digital twin,

    J. Liu, Z. Xu, W. Xu, L. Qi, Y . Han, and Z. Zhou, “Robotic disassembly sequence dynamic planning un- der uncertain irremovable condition using dueling deep q-network based on digital twin,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103132, 2026

    2026

  61. [69]

    Graph-driven single-robot multi-cognitive agent system architecture for human–robot collaborative disassembly,

    J. Lv, J. Si, W. Li, D. Gao, and J. Bao, “Graph-driven single-robot multi-cognitive agent system architecture for human–robot collaborative disassembly,”Robotics and Computer-Integrated Manufacturing, vol. 99, p. 103207, 2026

    2026

  62. [70]

    Large language models empower the reliability of disassembly in remanufacturing,

    L. Xia, J. Pang, C. Li, R. Wang, and P. Zheng, “Large language models empower the reliability of disassembly in remanufacturing,”Manufacturing Letters, vol. 41, pp. 1728–1733, 2024

    2024

  63. [71]

    Reinforcement learning with large lan- guage model for hybrid disassembly lines in remanu- facturing contexts,

    P. Ji, X. W. Guo, J. Wang, W. Wang, S. J. Qin, Y . Tang, and Q. Kang, “Reinforcement learning with large lan- guage model for hybrid disassembly lines in remanu- facturing contexts,” in2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, ...

    2024

  64. [72]

    Lever- aging large language models to empower bayesian networks for reliable human-robot collaborative disas- sembly sequence planning in remanufacturing,

    L. Xia, Y . Hu, J. Pang, X. Zhang, and C. Liu, “Lever- aging large language models to empower bayesian networks for reliable human-robot collaborative disas- sembly sequence planning in remanufacturing,”IEEE Transactions on Industrial Informatics, 2025

    2025

  65. [73]

    Human-robot collaborative disassembly in a cyber-physical remanufacturing system,

    Y . Hu, “Human-robot collaborative disassembly in a cyber-physical remanufacturing system,” Ph.D. disser- tation, Aston University, 2024

    2024

  66. [74]

    Large language model based multi-agents: A survey of progress and challenges,

    T. Guo, X. Chen, Y . Wang, R. Chang, S. Pei, N. V . Chawla, O. Wiest, and X. Zhang, “Large language model based multi-agents: A survey of progress and challenges,”arXiv preprint arXiv:2402.01680, 2024

    2024 arXiv

  67. [75]

    Llm-assisted reinforcement learning for u-shaped and circular hybrid disassembly line balanc- ing in iot-enabled smart manufacturing,

    X. Guo, C. Jiao, J. Wang, S. Qin, B. Hu, L. Qi, X. Lang, and Z. Zhang, “Llm-assisted reinforcement learning for u-shaped and circular hybrid disassembly line balanc- ing in iot-enabled smart manufacturing,”Electronics, vol. 14, no. 11, p. 2290, 2025

    2025

  68. [76]

    Llm-driven symbolic planning and hierarchical imitation learning for long-horizon deformable object assembly,

    J. Qi, L. Lu, F. Wang, H.-Y . Lee, D. Navarro-Alarcon, Z. Zhang, and P. Zhou, “Llm-driven symbolic planning and hierarchical imitation learning for long-horizon deformable object assembly,”Robotics and Computer- Integrated Manufacturing, vol. 97, p. 103096, 2026

    2026

  69. [77]

    How pretrained foundation models and cloud-fog au- tomation empower the recycling of electrical vehicles,

    S. Liu, D. Lan, J. Wang, D. Hu, Z. Pang, and H. Lyu, “How pretrained foundation models and cloud-fog au- tomation empower the recycling of electrical vehicles,” in2024 IEEE 22nd International Conference on Indus- trial Informatics (INDIN). IEEE, 2024, pp. 1–6

    2024

  70. [78]

    Revolutionizing battery disassembly: The design and implementation of a battery disassembly autonomous mobile manipulator robot (beam-1),

    Y . Peng, Z. Wang, Y . Zhang, S. Zhang, N. Cai, F. Wu, and M. Chen, “Revolutionizing battery disassembly: The design and implementation of a battery disassembly autonomous mobile manipulator robot (beam-1),” in 2024 IEEE/RSJ International Conference on Intelligent Robots and S...

    2024

  71. [79]

    Task-context-aware diffusion policy with language guidance for multi-task disassembly,

    J. H. Kang, S. Joshi, N. Dhanaraj, and S. K. Gupta, “Task-context-aware diffusion policy with language guidance for multi-task disassembly,” in2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). IEEE, 2025, pp. 609–616

    2025

  72. [80]

    Experience-driven neurosymbolic system for efficient robotic bolt disassembly,

    P. Chang, Z. Wang, Y . Peng, Z. He, and M. Chen, “Experience-driven neurosymbolic system for efficient robotic bolt disassembly,”Batteries, vol. 11, no. 9, p. 332, 2025

    2025

  73. [81]

    Vision-based robotic disassembly of aircraft engines with yolo-sam: a novel method for task orientation estimation,

    A. Moroncelli, S. Populus, A. Rossi, E. Carpanzano, and L. Roveda, “Vision-based robotic disassembly of aircraft engines with yolo-sam: a novel method for task orientation estimation,”CIRP Annals, 2025

    2025

  74. [82]

    Rescheduling human-robot collaboration tasks under dynamic disassembly scenarios: An mllm- kg collaboratively enabled approach,

    W. Yu, J. Lv, W. Zhuang, X. Pan, S. Wen, J. Bao, and X. Li, “Rescheduling human-robot collaboration tasks under dynamic disassembly scenarios: An mllm- kg collaboratively enabled approach,”Journal of Man- ufacturing Systems, vol. 80, pp. 20–37, 2025

    2025

  75. [83]

    Large language model-guided graph convolution network reasoning system for com- plex human-robot collaboration disassembly opera- tions,

    J. Xiao and S. Terzi, “Large language model-guided graph convolution network reasoning system for com- plex human-robot collaboration disassembly opera- tions,”Procedia CIRP, vol. 134, pp. 43–48, 2025

    2025

  76. [84]

    Gnn-llm hybrid cognitive architectures for generative task adaptation in multi- human multi-robot collaborative disassembly,

    X. Tong, K. Li, and J. Bao, “Gnn-llm hybrid cognitive architectures for generative task adaptation in multi- human multi-robot collaborative disassembly,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103169, 2026

    2026

  77. [85]

    Llm- powered operator advisor for human-robot collaboration in sustainable assembly-disassembly cells,

    M. Kheirabadi, E. Ghorbani, and S. Keivanpour, “Llm- powered operator advisor for human-robot collaboration in sustainable assembly-disassembly cells,” in2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS). IEEE, 2025, pp. 19–22

    2025

  78. [86]

    Llm-driven fmea for safe human- robot collaboration in disassembly,

    M. J. Alenjareghi, S. Keivanpour, Y . A. Chinniah, and S. Jocelyn, “Llm-driven fmea for safe human- robot collaboration in disassembly,” in2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS). IEEE, 2025, pp. 295–301

    2025

  79. [87]

    Proactive safety reasoning in human-robot collaboration in disassembly through llm-augmented stpa and fmea,

    M. J. Alenjareghi, F. Ghorbani, S. Keivanpour, Y . A. Chinniah, and S. Jocelyn, “Proactive safety reasoning in human-robot collaboration in disassembly through llm-augmented stpa and fmea,”Robotics and Computer- Integrated Manufacturing, vol. 98, p. 103162, 2026

    2026

  80. [88]

    Study on remanufacturing cleaning technology in me- chanical equipment remanufacturing process,

    W. Liu, B. Zhang, M. Z. Li, Y . Li, and H.-C. Zhang, “Study on remanufacturing cleaning technology in me- chanical equipment remanufacturing process,” inRe- engineering Manufacturing for Sustainability: Proceed- ings of the 20th CIRP International Conference on Life Cycle Engi...

    2013

  81. [89]

    The inte- gration of core cleaning and product serviceability into product modularization for the creation of an improved remanufacturing-product service system,

    J. A. Fadeyi, L. Monplaisir, and C. Aguwa, “The inte- gration of core cleaning and product serviceability into product modularization for the creation of an improved remanufacturing-product service system,”Journal of Cleaner Production, vol. 159, pp. 446–455, 2017

    2017

  82. [90]

    Clarifying the disagreements on various reuse options: Repair, recon- dition, refurbish and remanufacture,

    M. Gharfalkar, Z. Ali, and G. Hillier, “Clarifying the disagreements on various reuse options: Repair, recon- dition, refurbish and remanufacture,”Waste Manage- ment & Research, vol. 34, no. 10, pp. 995–1005, 2016

    2016

  83. [91]

    Optimiza- tion of reconditioning scheme for remanufacturing of used parts based on failure characteristics,

    Y . Wang, Z. Jiang, X. Hu, and C. Li, “Optimiza- tion of reconditioning scheme for remanufacturing of used parts based on failure characteristics,”Robotics and Computer-Integrated Manufacturing, vol. 61, p. 101833, 2020

    2020

  84. [92]

    An evaluation model of product upgradeability for remanufacture,

    K. Xing, M. Belusko, L. Luong, and K. Abhary, “An evaluation model of product upgradeability for remanufacture,”The International Journal of Advanced Manufacturing Technology, vol. 35, no. 1, pp. 1–14, 2007

    2007

  85. [93]

    Remanufacturing with upgrade pss for new sustainable business models,

    G. Copani and S. Behnam, “Remanufacturing with upgrade pss for new sustainable business models,”CIRP Journal of Manufacturing Science and Technology, vol. 29, pp. 245–256, 2020

    2020

  86. [94]

    Data-driven decision-making method for functional upgrade remanufacturing of used products based on multi-life customization scenarios,

    B. Wu, Z. Jiang, S. Zhu, H. Zhang, Y . Wang, and Y . Zhang, “Data-driven decision-making method for functional upgrade remanufacturing of used products based on multi-life customization scenarios,”Journal of Cleaner Production, vol. 334, p. 130238, 2022

    2022

  87. [95]

    A systematic review of additive manufacturing-based remanufacturing tech- niques for component repair and restoration,

    K. Kanishka and B. Acherjee, “A systematic review of additive manufacturing-based remanufacturing tech- niques for component repair and restoration,”Journal of Manufacturing Processes, vol. 89, pp. 220–283, 2023

    2023

  88. [96]

    Data- driven ecological performance evaluation for reman- ufacturing process,

    Z. Jiang, Z. Ding, H. Zhang, W. Cai, and Y . Liu, “Data- driven ecological performance evaluation for reman- ufacturing process,”Energy Conversion and Manage- ment, vol. 198, p. 111844, 2019

    2019

  89. [97]

    A knowledge graph-based intelligent planning method for remanufacturing processes of used parts,

    S. Zhu, L. Gao, Z. Jiang, W. Yan, and H. Zhang, “A knowledge graph-based intelligent planning method for remanufacturing processes of used parts,”Journal of Engineering Design, pp. 1–28, 2025

    2025

  90. [98]

    Assessing the uncertainty and robustness of the laptop refurbishing software,

    C. Lu, J. Wu, S. Ali, and M. L. Olsen, “Assessing the uncertainty and robustness of the laptop refurbishing software,” in2025 IEEE Conference on Software Test- ing, Verification and Validation (ICST). IEEE, 2025, pp. 406–416

    2025

  91. [99]

    Development of reman- ufacturing systems at original equipment remanufactur- ers,

    P. Wlazlak and K. Johansen, “Development of reman- ufacturing systems at original equipment remanufactur- ers,”International Journal on Interactive Design and Manufacturing (IJIDeM), pp. 1–21, 2025

    2025

  92. [100]

    Leveraging generative ai prompt programming for human-robot collaborative assembly,

    C. Konstantinou, D. Antonarakos, P. Angelakis, C. Gk- ournelos, G. Michalos, and S. Makris, “Leveraging generative ai prompt programming for human-robot collaborative assembly,”Procedia CIRP, vol. 128, pp. 621–626, 2024

    2024

  93. [101]

    Towards cognition-augmented human-centric assembly: A vi- sual computation perspective,

    J. Pang, P. Zheng, J. Fan, and T. Liu, “Towards cognition-augmented human-centric assembly: A vi- sual computation perspective,”Robotics and Computer- Integrated Manufacturing, vol. 91, p. 102852, 2025

    2025

  94. [102]

    Probing ar-assisted seamless hrc assembly for industry 5.0: Multi-modal mutual cognition and llm- driven knowledge reasoning,

    Y . Ma, D. Tang, H. Zhu, Q. Cai, Z. Zhang, L. Wang, and C. Liu, “Probing ar-assisted seamless hrc assembly for industry 5.0: Multi-modal mutual cognition and llm- driven knowledge reasoning,”Robotics and Computer- Integrated Manufacturing, vol. 97, p. 103112, 2026

    2026

  95. [103]

    Llm based autonomous agent of human-robot collaboration for aerospace wire harnessing assem- bly,

    Y . Wang, Q. Guo, L. Zheng, B. Wang, P. Zheng, and Z. Qi, “Llm based autonomous agent of human-robot collaboration for aerospace wire harnessing assem- bly,”Robotics and Computer-Integrated Manufacturing, vol. 97, p. 103120, 2026

    2026

  96. [104]

    The strategic value of design for re- manufacturing: a case study of professional imaging equipment,

    N. Boorsma, D. Peck, T. Bakker, C. Bakker, and R. Balkenende, “The strategic value of design for re- manufacturing: a case study of professional imaging equipment,”Journal of Remanufacturing, vol. 12, no. 2, pp. 187–212, 2022

    2022

  97. [105]

    Reman co-design: A combined design and remanufacturing optimization framework for the sustainable design of high-value components,

    M. Behtash, X. Liu, M. Davied, T. Thompson, R. Bur- jes, M. Lee, P. Wang, and C. Hu, “Reman co-design: A combined design and remanufacturing optimization framework for the sustainable design of high-value components,”Journal of Mechanical Design, vol. 146, no. 2, p. 020901, 2024

    2024

  98. [106]

    Incorporating design for remanufacturing in the early design stage: a design management perspec- tive,

    N. Boorsma, R. Balkenende, C. Bakker, T. Tsui, and D. Peck, “Incorporating design for remanufacturing in the early design stage: a design management perspec- tive,”Journal of Remanufacturing, vol. 11, no. 1, pp. 25–48, 2021

    2021

  99. [107]

    Generative ai for cad automation: Leveraging large language models for 3d modelling,

    S. Kumar, S. Kapoor, H. Vardhan, and Y . Zhao, “Generative ai for cad automation: Leveraging large language models for 3d modelling,”arXiv preprint arXiv:2508.00843, 2025

    2025 arXiv

  100. [108]

    Lever- aging an llm for sustainable product development: an eco-design framework,

    M. Ashkbous, L. C ˆot´e, and S. Keivanpour, “Lever- aging an llm for sustainable product development: an eco-design framework,” in2025 IEEE 5th In- ternational Conference on Human-Machine Systems (ICHMS). IEEE, 2025, pp. 23–27

    2025

  101. [109]

    Dr-rag: Domain-rule-based retrieval-augmented generation for aviation digital model design,

    X. Xiong, H. Cai, H. Yu, B. Shen, and P. Hu, “Dr-rag: Domain-rule-based retrieval-augmented generation for aviation digital model design,”Advanced Engineering Informatics, vol. 68, p. 103688, 2025

    2025

  102. [110]

    Multilingual graph retrieval-augmented generation for product design using design knowledge,

    H. Zhang, T. Wang, Z. Liang, Z. Huang, C. Chen, and L. Cheng, “Multilingual graph retrieval-augmented generation for product design using design knowledge,” Journal of Engineering Design, pp. 1–32, 2025

    2025

  103. [111]

    Proposing a model based on deep reinforcement learning for real-time scheduling of collaborative cus- tomization remanufacturing,

    S. A. Yazdanparast, S. H. Zegordi, and T. Khat- ibi, “Proposing a model based on deep reinforcement learning for real-time scheduling of collaborative cus- tomization remanufacturing,”Robotics and Computer- Integrated Manufacturing, vol. 94, p. 102980, 2025

    2025

  104. [112]

    A batch production scheduling problem in a reconfigurable hybrid manufacturing- remanufacturing system,

    B. Vahedi-Nouri, M. Rohaninejad, Z. Hanz ´alek, and M. Foumani, “A batch production scheduling problem in a reconfigurable hybrid manufacturing- remanufacturing system,”Computers & Industrial En- gineering, vol. 204, p. 111099, 2025

    2025

  105. [113]

    Multifactory remanufacturing process optimiza- tion considering worker scheduling,

    L. Zhou, X. Guo, Q. Liu, J. Wang, S. Qin, and L. Qi, “Multifactory remanufacturing process optimiza- tion considering worker scheduling,”IEEE Transactions on Computational Social Systems, 2025

    2025

  106. [114]

    A hybrid genetic algorithm with multiple decoding meth- ods for energy-aware remanufacturing system schedul- ing problem,

    W. Wang, G. Tian, H. Zhang, Z. Li, and L. Zhang, “A hybrid genetic algorithm with multiple decoding meth- ods for energy-aware remanufacturing system schedul- ing problem,”Robotics and Computer-Integrated Man- ufacturing, vol. 81, p. 102509, 2023

    2023

  107. [115]

    Y . Fu, Z. Zhang, P. Liang, G. Tian, and C. Zhang, “Integrated remanufacturing scheduling of disassem- bly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches,”Engineering optimization, vol....

    2018

  108. [116]

    Modeling and scheduling for remanufacturing systems with disassembly, reprocessing, and reassembly consid- ering total energy consumption,

    W. Wang, G. Tian, H. Zhang, K. Xu, and Z. Miao, “Modeling and scheduling for remanufacturing systems with disassembly, reprocessing, and reassembly consid- ering total energy consumption,”Environmental Science and Pollution Research, pp. 1–17, 2021

    2021

  109. [117]

    An llm-based knowledge and function-augmented approach for optimal design of remanufacturing process,

    H. Zhang, W. Yan, H. Hu, X. Zhang, Q. Liu, H. Xia, Y . Zhang, and Y . Lin, “An llm-based knowledge and function-augmented approach for optimal design of remanufacturing process,”Advanced Engineering Infor- matics, vol. 65, p. 103206, 2025

    2025

  110. [118]

    Data- driven motion planning: A survey on deep neural networks, reinforcement learning, and large language model approaches,

    G. P. De Carvalho, T. Sawanobori, and T. Horii, “Data- driven motion planning: A survey on deep neural networks, reinforcement learning, and large language model approaches,”IEEE Access, 2025

    2025

  111. [119]

    Leveraging large language models in human-robot interaction: A critical analysis of potential and pitfalls,

    J. Atuhurra, “Leveraging large language models in human-robot interaction: A critical analysis of potential and pitfalls,”arXiv preprint arXiv:2405.00693, 2024

    2024 arXiv

  112. [120]

    Transformation of in- dustrial robotics with natural language models: Recent progress and future prospects,

    Z. Yu, P. Zhang, and J. Shi, “Transformation of in- dustrial robotics with natural language models: Recent progress and future prospects,”Robotics and Computer- Integrated Manufacturing, vol. 97, p. 103113, 2026

    2026

  113. [121]

    Applying ontologies and knowledge augmented large language models to industrial automation: A decision-making guidance for achieving human-robot collaboration in industry 5.0,

    J. Oyekan, C. Turner, M. Bax, and E. Graf, “Applying ontologies and knowledge augmented large language models to industrial automation: A decision-making guidance for achieving human-robot collaboration in industry 5.0,”arXiv preprint arXiv:2505.18553, 2025

    2025 arXiv

  114. [122]

    A study on multi-modal llm reasoning for defect detection,

    A. A. Tulbure, D. P. Danciu, E. H. Dulf, and A. A. Tulbure, “A study on multi-modal llm reasoning for defect detection,” in2024 IEEE 30th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2024, pp. 153–157

    2024

  115. [123]

    Reasoning grasping via multimodal large language model,

    S. Jin, J. Xu, Y . Lei, and L. Zhang, “Reasoning grasping via multimodal large language model,”arXiv preprint arXiv:2402.06798, 2024

    2024 arXiv

  116. [124]

    Alchemist: Llm-aided end-user development of robot applications,

    U. B. Karli, J.-T. Chen, V . N. Antony, and C.-M. Huang, “Alchemist: Llm-aided end-user development of robot applications,” inProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024, pp. 361–370

    2024

  117. [125]

    Towards zero-shot robot tool manipulation in indus- trial context: A modular vlm framework enhanced by multimodal affordance representation,

    Q. Zhou, Y . Gu, J. Li, B. Feng, B. Li, and Y . Bi, “Towards zero-shot robot tool manipulation in indus- trial context: A modular vlm framework enhanced by multimodal affordance representation,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103161, 2026

    2026

  118. [126]

    Plan-seq-learn: Language model guided rl for solving long horizon robotics tasks,

    M. Dalal, T. Chiruvolu, D. Chaplot, and R. Salakhut- dinov, “Plan-seq-learn: Language model guided rl for solving long horizon robotics tasks,”arXiv preprint arXiv:2405.01534, 2024

    2024 arXiv

  119. [127]

    You are my eyes: Integrating human intelligence and llms in ar-assisted motion planning for industrial mobile robots,

    S. Liu, J. Xie, X. Wang, and X. Qiao, “You are my eyes: Integrating human intelligence and llms in ar-assisted motion planning for industrial mobile robots,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103174, 2026

    2026

  120. [128]

    Vision-language-action models: Concepts, progress, applications and challenges,

    R. Sapkota, Y . Cao, K. I. Roumeliotis, and M. Karkee, “Vision-language-action models: Concepts, progress, applications and challenges,”arXiv preprint arXiv:2505.04769, 2025

    2025

  121. [129]

    Openvla: An open-source vision-language- action model,

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Bal- akrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. San- ketiet al., “Openvla: An open-source vision-language- action model,”arXiv preprint arXiv:2406.09246, 2024

    2024 arXiv

  122. [130]

    Research on task decomposition and motion trajectory optimization of robotic arm based on vla large model,

    W. Lu, X. Wu, S. Gao, W. He, Q. Zhao, L. Zhang, M. Li, and W. Zhang, “Research on task decomposition and motion trajectory optimization of robotic arm based on vla large model,” in2024 5th International Confer- ence on Machine Learning and Computer Application (ICMLCA). IEEE, ...

    2024

  123. [131]

    Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation,

    J. Wen, Y . Zhu, J. Li, M. Zhu, Z. Tang, K. Wu, Z. Xu, N. Liu, R. Cheng, C. Shenet al., “Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation,”IEEE Robotics and Automation Letters, 2025

    2025

  124. [132]

    Spatialvla: Exploring spatial representations for visual-language- action model,

    D. Qu, H. Song, Q. Chen, Y . Yao, X. Ye, Y . Ding, Z. Wang, J. Gu, B. Zhao, D. Wanget al., “Spatialvla: Exploring spatial representations for visual-language- action model,”arXiv preprint arXiv:2501.15830, 2025

    2025 arXiv

  125. [133]

    Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,

    A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Man- dlekar, A. Jainet al., “Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,” in2024 IEEE International Confer- ence on Robotics and Aut...

    2024

  126. [134]

    Vla- os: Structuring and dissecting planning representations and paradigms in vision-language-action models,

    C. Gao, Z. Liu, Z. Chi, J. Huang, X. Fei, Y . Hou, Y . Zhang, Y . Lin, Z. Fang, Z. Jianget al., “Vla- os: Structuring and dissecting planning representations and paradigms in vision-language-action models,”arXiv preprint arXiv:2506.17561, 2025

    2025 arXiv

  127. [135]

    Integrating disam- biguation and user preferences into large language models for robot motion planning,

    M. Abugurain and S. Park, “Integrating disam- biguation and user preferences into large language models for robot motion planning,”arXiv preprint arXiv:2404.14547, 2024

    2024 arXiv

  128. [136]

    Palm-e: An embodied multimodal language model,

    D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdh- ery, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang et al., “Palm-e: An embodied multimodal language model,” 2023

    2023

  129. [137]

    Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators,

    P. Wu, Y . Shentu, Z. Yi, X. Lin, and P. Abbeel, “Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Sys- tems (IROS). IEEE, 2024, pp. 12 156–12 163

    2024

  130. [138]

    Learning when to see and when to feel: Adaptive vision-torque fusion for contact-aware manipulation,

    J. Lei, C. Liu, Y . She, X. Liang, and M. Zheng, “Learning when to see and when to feel: Adaptive vision-torque fusion for contact-aware manipulation,” arXiv preprint arXiv:2604.01414, 2026

    2026

  131. [139]

    Sustainable management of electric vehicle battery remanufactur- ing: A systematic literature review and future direc- tions,

    A. Neri, M. A. Butturi, and R. Gamberini, “Sustainable management of electric vehicle battery remanufactur- ing: A systematic literature review and future direc- tions,”Journal of Manufacturing Systems, vol. 77, pp. 859–874, 2024

    2024

  132. [140]

    Cost-effective sup- ply chain for electric vehicle battery remanufacturing,

    L. Li, F. Dababneh, and J. Zhao, “Cost-effective sup- ply chain for electric vehicle battery remanufacturing,” Applied energy, vol. 226, pp. 277–286, 2018

    2018

  133. [141]

    Global ev data explorer,

    International Energy Agency, “Global ev data explorer,”

  134. [142]

    Electric vehicle batteries — chapter 9 in global ev outlook 2025,

    ——, “Electric vehicle batteries — chapter 9 in global ev outlook 2025,” https://www.iea.org/reports/ global-ev-outlook-2025/electric-vehicle-batteries, International Energy Agency, Paris, Tech. Rep. Chapter 9, 2025

    2025

  135. [143]

    Intelligent disassembly of electric-vehicle batteries: a forward-looking overview,

    K. Meng, G. Xu, X. Peng, K. Youcef-Toumi, and J. Li, “Intelligent disassembly of electric-vehicle batteries: a forward-looking overview,”Resources, Conservation and Recycling, vol. 182, p. 106207, 2022

    2022

  136. [144]

    A system dynamics model for end-of-life management of electric vehicle batteries in the us: Comparing the cost, carbon, and material requirements of remanufacturing and recycling,

    D. Kamath, S. Moore, R. Arsenault, and A. Anctil, “A system dynamics model for end-of-life management of electric vehicle batteries in the us: Comparing the cost, carbon, and material requirements of remanufacturing and recycling,”Resources, Conservation and Recycling, vol. 19...

    2023

  137. [145]

    Robotic disassembly of snap-fit plug connectors in end-of-life electric vehicle batter- ies,

    J. Huang, Q. Huang, Y . Zeng, M. Tan, Z. Peng, H. Song, X. Ao, and D. Pham, “Robotic disassembly of snap-fit plug connectors in end-of-life electric vehicle batter- ies,”Robotics and Computer-Integrated Manufacturing, vol. 98, p. 103183, 2026

    2026

  138. [146]

    Robotic removal and collection of screws in collaborative disassembly of end-of-life electric vehicle batteries,

    M. Tan, J. Huang, X. Jiang, Y . Fang, Q. Liu, and D. Pham, “Robotic removal and collection of screws in collaborative disassembly of end-of-life electric vehicle batteries,”Biomimetics, vol. 10, no. 8, p. 553, 2025

    2025

  139. [147]

    Towards a green electromobility transition: a systematic review of the state of the art on electric vehicle battery systems dis- assembly,

    D. Hertel, G. Br ¨aunig, and M. Th¨urer, “Towards a green electromobility transition: a systematic review of the state of the art on electric vehicle battery systems dis- assembly,”Journal of Manufacturing Systems, vol. 74, pp. 387–396, 2024

    2024

  140. [148]

    Global e-waste monitor 2024: Electronic waste rising five times faster than documented e- waste recycling,

    UNITAR, “Global e-waste monitor 2024: Electronic waste rising five times faster than documented e- waste recycling,” 2024. [Online]. Available: https: //ewastemonitor.info/the-global-e-waste-monitor-2024/

    2024

  141. [149]

    Environmental and economic impacts of e-waste recycling: A systematic review,

    J. Lee, H. Choi, and J. Kim, “Environmental and economic impacts of e-waste recycling: A systematic review,”Chemical Engineering Journal, vol. 494, p. 152917, 2024

    2024

  142. [150]

    A hybrid task- constrained motion planning for collaborative robots in intelligent remanufacturing,

    W. Liu, C. Liu, X. Liang, and M. Zheng, “A hybrid task- constrained motion planning for collaborative robots in intelligent remanufacturing,”Mechatronics, vol. 102, p. 103222, 2024

    2024

  143. [151]

    Electric motor market size and share — industry report, 2030,

    G. V . Research, “Electric motor market size and share — industry report, 2030,” 2024. [Online]. Available: https://www.grandviewresearch. com/industry-analysis/electric-motor-market

    2024

  144. [152]

    A review of circular economy research for electric motors and the role of industry 4.0 technologies,

    D. Tiwari, J. Miscandlon, A. Tiwari, and G. W. Jewell, “A review of circular economy research for electric motors and the role of industry 4.0 technologies,” Sustainability, vol. 13, no. 17, p. 9668, 2021

    2021

  145. [153]

    A circular economy approach for recycling electric motors in the end-of-life vehicles: A literature review,

    Z. Li, A. S. Hamidi, Z. Yan, A. Sattar, S. Hazra, J. Soulard, C. Guest, S. H. Ahmed, and F. Tailor, “A circular economy approach for recycling electric motors in the end-of-life vehicles: A literature review,” Resources, conservation and recycling, vol. 205, p. 107582, 2024

    2024

  146. [154]

    Circularity potential of electric motors in e-mobility: methods, technologies, challenges,

    A. Di Gerlando, M. Gobbi, M. C. Magnanini, G. Mastinu, R. Palazzetti, A. Sattar, and T. Tolio, “Circularity potential of electric motors in e-mobility: methods, technologies, challenges,”Journal of Reman- ufacturing, vol. 14, no. 2, pp. 315–357, 2024

    2024

  147. [155]

    Low- cost real-time monitoring of electric motors for the industry 4.0,

    L. Magadan, F. Su ´arez, J. Granda, and D. Garc´ıa, “Low- cost real-time monitoring of electric motors for the industry 4.0,”Procedia Manufacturing, vol. 42, pp. 393–398, 2020

    2020

  148. [156]

    A survey of hal- lucination in large foundation models,

    V . Rawte, A. Sheth, and A. Das, “A survey of hal- lucination in large foundation models,”arXiv preprint arXiv:2309.05922, 2023

    2023 arXiv

  149. [157]

    Hallucination is inevitable: An innate limitation of large language models,

    Z. Xu, S. Jain, and M. Kankanhalli, “Hallucination is inevitable: An innate limitation of large language models,”arXiv preprint arXiv:2401.11817, 2024

    2024 arXiv

  150. [158]

    A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

    L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qinet al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,”ACM Trans- actions on Information Systems, vol. 43, no. 2, pp. 1–55, 2025

    2025

  151. [159]

    A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing,

    C. Zhang, Q. Xu, Y . Yu, G. Zhou, K. Zeng, F. Chang, and K. Ding, “A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing,”Robotics and Computer- Integrated Manufacturing, vol. 92, p. 102883, 2025

    2025

  152. [160]

    Rethinking interpretability in the era of large language models,

    C. Singh, J. P. Inala, M. Galley, R. Caruana, and J. Gao, “Rethinking interpretability in the era of large language models,”arXiv preprint arXiv:2402.01761, 2024

    2024 arXiv

  153. [161]

    Xai meets llms: A survey of the relation between explainable ai and large language models,

    E. Cambria, L. Malandri, F. Mercorio, N. Nobani, and A. Seveso, “Xai meets llms: A survey of the relation between explainable ai and large language models,” arXiv preprint arXiv:2407.15248, 2024

    2024 arXiv

  154. [162]

    Explainable ai (xai): Core ideas, techniques, and so- lutions,

    R. Dwivedi, D. Dave, H. Naik, S. Singhal, R. Omer, P. Patel, B. Qian, Z. Wen, T. Shah, G. Morganet al., “Explainable ai (xai): Core ideas, techniques, and so- lutions,”ACM computing surveys, vol. 55, no. 9, pp. 1–33, 2023

    2023

  155. [163]

    Datasets for large language models: A comprehensive survey,

    Y . Liu, J. Cao, C. Liu, K. Ding, and L. Jin, “Datasets for large language models: A comprehensive survey,” arXiv preprint arXiv:2402.18041, 2024

    2024 arXiv

  156. [164]

    A survey on knowledge distillation of large language models,

    X. Xu, M. Li, C. Tao, T. Shen, R. Cheng, J. Li, C. Xu, D. Tao, and T. Zhou, “A survey on knowledge distillation of large language models,”arXiv preprint arXiv:2402.13116, 2024

    2024 arXiv

  157. [165]

    Beyond effi- ciency: A systematic survey of resource-efficient large language models,

    G. Bai, Z. Chai, C. Ling, S. Wang, J. Lu, N. Zhang, T. Shi, Z. Yu, M. Zhu, Y . Zhanget al., “Beyond effi- ciency: A systematic survey of resource-efficient large language models,”arXiv preprint arXiv:2401.00625, 2024

    2024 arXiv

  158. [166]

    A survey on large language model (llm) security and pri- vacy: The good, the bad, and the ugly,

    Y . Yao, J. Duan, K. Xu, Y . Cai, Z. Sun, and Y . Zhang, “A survey on large language model (llm) security and pri- vacy: The good, the bad, and the ugly,”High-Confidence Computing, vol. 4, no. 2, p. 100211, 2024

    2024

  159. [167]

    Security and privacy challenges of large language models: A survey,

    B. C. Das, M. H. Amini, and Y . Wu, “Security and privacy challenges of large language models: A survey,” ACM Computing Surveys, vol. 57, no. 6, pp. 1–39, 2025

    2025

  160. [168]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning,

    D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Biet al., “Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning,”arXiv preprint arXiv:2501.12948, 2025

    2025 arXiv

  161. [169]

    Deepseek- v3 technical report,

    A. Liu, B. Feng, B. Xue, B. Wang, B. Wu, C. Lu, C. Zhao, C. Deng, C. Zhang, C. Ruanet al., “Deepseek- v3 technical report,”arXiv preprint arXiv:2412.19437, 2024

    2024 arXiv

  162. [170]

    Fine-tuning large language models with user-level differential privacy,

    Z. Charles, A. Ganesh, R. McKenna, H. B. McMahan, N. Mitchell, K. Pillutla, and K. Rush, “Fine-tuning large language models with user-level differential privacy,” arXiv preprint arXiv:2407.07737, 2024

    2024 arXiv

  163. [171]

    Federatedscope- llm: A comprehensive package for fine-tuning large language models in federated learning,

    W. Kuang, B. Qian, Z. Li, D. Chen, D. Gao, X. Pan, Y . Xie, Y . Li, B. Ding, and J. Zhou, “Federatedscope- llm: A comprehensive package for fine-tuning large language models in federated learning,” inProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data...

    2024

  164. [172]

    Privacy and security challenges in large language models,

    V . Rathod, S. Nabavirazavi, S. Zad, and S. S. Iyen- gar, “Privacy and security challenges in large language models,” in2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025, pp. 00 746–00 752

    2025

  165. [173]

    Vision-language-action models for selective robotic disassembly: A case study on critical component extrac- tion from desktops,

    C. Liu, S. Tian, S. Behdad, X. Liang, and M. Zheng, “Vision-language-action models for selective robotic disassembly: A case study on critical component extrac- tion from desktops,”arXiv preprint arXiv:2512.04446, 2025

    2025

  166. [174]

    Self- vla: A skill enhanced agentic vision-language-action framework for contact-rich disassembly,

    C. Liu, S. Tian, X. Liang, and M. Zheng, “Self- vla: A skill enhanced agentic vision-language-action framework for contact-rich disassembly,”arXiv preprint arXiv:2603.11080, 2026

    2026

  167. [175]

    3d gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimkuehler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” vol. 42, no. 4, Jul. August 2023. [Online]. Available: https://doi.org/10.1145/3592433

    2023 doi

  168. [176]

    Vla-rl: Towards masterful and general robotic manipulation with scalable reinforce- ment learning,

    G. Lu, W. Guo, C. Zhang, Y . Zhou, H. Jiang, Z. Gao, Y . Tang, and Z. Wang, “Vla-rl: Towards masterful and general robotic manipulation with scalable reinforce- ment learning,”arXiv preprint arXiv:2505.18719, 2025

    2025 arXiv

  169. [177]

    π ∗ 0.6: a vla that learns from experi- ence,

    A. Amin, R. Aniceto, A. Balakrishna, K. Black, K. Con- ley, G. Connors, J. Darpinian, K. Dhabalia, J. DiCarlo, D. Driesset al., “π ∗ 0.6: a vla that learns from experi- ence,”arXiv preprint arXiv:2511.14759, 2025

    2025 arXiv

  170. [2025]

    Available: https://www.iea.org/ data-and-statistics/data-tools/global-ev-data-explorer

    [Online]. Available: https://www.iea.org/ data-and-statistics/data-tools/global-ev-data-explorer

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.