Pith. sign in

REVIEW 3 major objections 5 minor 45 references

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read A three-layer reference architecture for smart manufacturing assigns fast coordination to task-trained MARL, semantic reasoning to LLMs, and deadlines and safety to an independent assured execution layer.

desk verdict A useful reference architecture and decision framework, but the MARL 'supported' ratings in Table 1 are inherited from a self-cited review rather than primary evidence, and the paper should fix or soften them before it is treated as authoritative. read the letter →

arxiv 2608.07148 v1 pith:QAV2KKAK submitted 2026-08-07 cs.AI

Fouad Bahrpeyma , Dirk Reichelt This is my paper · ORCID
classification cs.AI
keywords LargeLanguageModelsMultiagentReinforcementLearningSmartManufacturingAgenticAIFoundationDeploymentReadinessIndustry4.0Dec-POMDP
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 asks where large language models should attach to a multiagent reinforcement learning system for smart manufacturing, rather than treating the two technologies as rivals. It argues, on the evidence it reviews, that task-trained cooperative MARL is the best-supported mechanism for frequent, structured, decentralized coordination, while LLM components are best supported for semantic interpretation, reward drafting, human interaction, and slower supervisory planning. The principal contribution is a three-layer reference architecture: LLM semantic reasoning above a MARL adaptive control core, with an independent layer that enforces deadlines and safety. A descriptive notation called the LLM-Augmented Dec-POMDP records the four possible attachment points through a tuple $\Phi$. The paper adds a decision framework for choosing attachments and a readiness assessment finding that most current manufacturing evidence is simulation-level.

What carries the argument

The load-bearing object is the three-layer reference architecture, and its comparative notation is the LLM-Augmented Dec-POMDP, $M^L = \langle N, S, \{A_i\}, T, \{R_i\}, \{O_i\}, \Omega, \gamma, L, \Phi\rangle$, where $\Phi = (\phi_{\mathrm{policy}}, \phi_{\mathrm{reward}}, \phi_{\mathrm{comm}}, \phi_{\mathrm{plan}})$ is a tuple of attachment descriptors, each either null or an LLM-parameterized function. The machinery does not alter the Dec-POMDP's assumptions; it records where an LLM enters a MARL-centered system and which conventional element it affects, so that architectures can be compared precisely. The argument is carried by a conditional capability profile that separates native mechanism, practical evidence, formal guarantee, and engineering maturity for two baseline configurations, and by a decision framework that routes a manufacturing problem to the attachment whose timescale and evidence match the requirement.

What would settle it

For example, a flexible job-shop simulator could host a normalized head-to-head with matched observations, action spaces, agent count, hardware, a declared decision deadline, and identical seeds; if an LLM-only controller with constrained decoding meets the deadline and matches or beats a task-trained MARL policy on makespan and on a post-reconfiguration transfer test, the Layer 2 MARL default for that application class is falsified.

Watch

Extended reading notes

Core claim

Under the reviewed evidence, conventional MARL generally supports frequent, structured, decentralized coordination after task-specific training, while LLM components are promising for semantic interpretation, reward drafting, human interaction, and slower supervisory planning. Current LLM-only manufacturing controllers do not establish equivalence for strict real-time, decentralized, safety-critical control, and the paper is explicit that this is a bound on the evidence, not a proof of impossibility. Layer 2 is therefore assigned to MARL as a conditional default derived from the evidence, not as a consequence of choosing MARL as the analytical baseline. The three-layer architecture assigns semantic reasoning to a selected LLM configuration for planning and interaction, adaptive cooperative control to a selected MARL configuration for coordination, credit assignment, and closed-loop learning, and assured execution to classical controllers, runtime monitors, action constraints, and, where required, a certified safety PLC. The LLM-Augmented Dec-POMDP is a descriptive comparative notation for this architecture and is not proposed as a new decision process class or algorithm.

Load-bearing premise

The entire empirical grounding of the three-layer architecture depends on the correctness and impartiality of the capability profile in Table 1, which is an authorial synthesis from a non-preregistered, iterative literature search with no inter-rater reliability measurement.

Editorial extensions

If this is right

  • If the architecture is right, manufacturing control stacks should keep task-trained MARL policies in the real-time coordination loop and place LLM calls either offline during training or at a slower planning epoch, not at every decision step.
  • LLM-only or hybrid controllers should be evaluated under matched observations, action spaces, agent count, hardware, deadlines, safety layer, evaluation seeds, and adaptation budget before being accepted as equivalent to MARL for strict real-time control.
  • A natural language communication channel within Layer 2 should be left null whenever the per-step decision budget cannot absorb an LLM call, falling back to numeric MARL communication or none.
  • An independently assured execution layer is necessary for deadlines and safety regardless of the Layer 1 and Layer 2 choices, because neither LLMs nor MARL provide safety by default.
  • No current work instantiates all three layers with a digital twin substrate; the architecture's most direct consequence is that this complete instantiation is the next step to test.

Reading between the lines

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

  • A natural next test, not run in the paper, is a matched comparison on a specific reconfigurable cell: if a fine-tuned small LLM policy with constrained decoding meets sub-second deadlines and matches or beats a task-trained MARL policy on makespan and on post-reconfiguration transfer, the Layer 2 default would shift for that application class without invalidating the Dec-POMDP formulation.
  • The paper treats readable rationales as an interface capability; an implicit consequence is that any use of those rationales for safety decisions requires a separate faithfulness evaluation that the architecture does not yet specify.
  • The inverse branch, in which MARL trains teams of LLM agents, is deliberately excluded as evidence about factory control; a promising extension would apply that training machinery to manufacturing planning agents, where its credit-assignment tools could address the open problem of adapting language agents to plant tasks.
  • The readiness snapshot implies a testable prediction: without a shared manufacturing benchmark, published LLM-plus-MARL manufacturing work should remain largely simulation-level, and the paper's reporting checklist could be used to track whether that changes.
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. This paper proposes a MARL-centered reference architecture for augmenting smart-manufacturing control with large language models. It derives six manufacturing demands, positions cooperative MARL as a Dec-POMDP/CTDE baseline, organizes the LLM+MARL literature into four attachment points (policy, reward, communication, planning), builds a conditional capability profile (Table 1) separating native mechanism, reported performance, formal guarantee, and engineering maturity, and proposes a three-layer architecture: LLM semantic reasoning, MARL adaptive cooperative control, and independently assured execution. A descriptive notation (LLM-Augmented Dec-POMDP) and a decision framework with a reporting checklist are included. The paper is carefully hedged and presents the architecture as a conditional, testable hypothesis rather than a universal ranking.

Significance. If the evidence base were fully auditable, the paper would be a valuable organizing framework for a rapidly growing area. Its strengths include a clean attachment taxonomy, a comparative notation that does not overclaim theoretical novelty, a minimum reporting checklist (Table 4) that could help normalize future comparisons, and an explicit readiness scale (Section 9). The paper also credits the possibility that its MARL default could be falsified by future evidence (Section 11). However, the significance is currently constrained by the reliability of Table 1, which is an authorial synthesis with no inter-rater reliability and, for several load-bearing rows, does not cite the primary evidence required by the paper's own decision rule.

major comments (3)
  1. [Section 4.3/Table 1, rows C5–C7; Section 5.1] The MARL 'P:S' ratings in rows C5 (closed-loop task learning), C6 (cooperative coordination), and C7 (credit assignment) do not meet the paper's own evidence threshold for S, defined in Section 1.4 as convergent evidence from at least two independent primary studies or benchmark families. The representative citations for these rows are either LLM papers ([Shinn et al., 2023], [Zhang et al., 2024b], [Slumbers et al., 2024]) or the authors' own prior review ([Bahrpeyma and Reichelt, 2022]); none supplies the required primary MARL performance evidence. Appendix A's ledger covers only the nine readiness-corpus works and explicitly disclaims being a mechanical derivation. Because Section 5.1 states that Layer 2 is assigned to MARL 'derived from current evidence,' the central claim is currently supported by ratings the manuscript itself cannot trace. The two-coder recoding the authors prescribe is the right remedy, but it has not been executed; until then, the ratings should be downgraded to M or L under the paper's own rubric or the primary studies cited.
  2. [Section 1.3 and Section 2.1; Table 1] The paper repeatedly treats the authors' prior review [Bahrpeyma and Reichelt, 2022] as an established evidence base for MARL applications and algorithms, and Table 1 uses that review as a load-bearing citation for the MARL S ratings in C5–C7. This is not objectionable simply because it is a self-citation; it is a problem because the current manuscript asks the reader to accept a rating hierarchy that it does not derive from verifiable primary sources. The sentence in Section 1.3 that the MARL base 'is treated here as given' makes the circularity explicit. Please provide the specific primary studies behind the S ratings, or explicitly relabel these ratings as inherited from the prior review and subject to its limitations.
  3. [Section 1.4 and Section 10] The capability profile is, by the paper's own admission, an authorial synthesis with no inter-rater reliability, produced from a non-preregistered, iterative literature search. The paper also notes in Section 10 that the readiness scale has not been validated against practitioner judgments. These limitations are disclosed honestly, but because the three-layer architecture is derived from Table 1, they are load-bearing rather than peripheral. The authors should either perform the independent two-coder recoding they prescribe, or present the architecture explicitly as a single-author design hypothesis whose evidence ratings require independent audit before being used to justify a MARL default.
minor comments (5)
  1. [Section 3.3] The heading 'LLMs as an communication between agents medium' contains a grammatical error; it should be 'LLMs as a communication medium between agents'.
  2. [Section 1.4] The decision rules for practical evidence (S/M/L/U) are stated in Section 1.4, but the definitions of the native-mechanism (N) and formal-guarantee (G) columns first appear only in Section 4.3; moving all four scales into Section 1.4 would make Table 1 auditable earlier.
  3. [Section 5.2] The claim 'No existing work is known to instantiate all three layers' is based on the non-exhaustive review; consider replacing 'known' with 'reviewed here' to avoid an unverifiable completeness assertion.
  4. [Section 9.2/Table 6] The readiness snapshot correctly restricts itself to reported validation settings, but the 'Level 4 production example [Bédorf et al., 2024]' mentioned in the text is not in the manufacturing corpus table; please make explicit why it is cited only as an existence proof outside the nine scored works.
  5. [Section 10] The prompt-injection discussion is valuable, but it should be explicitly linked to the reference architecture's Layer 3 assurance responsibilities, since the paper elsewhere argues that safety comes from an independent execution layer.

Circularity Check

1 steps flagged · score 4.0 of 10

The Layer 2 MARL default rests on Table 1 ratings whose decisive MARL rows (C5–C7) are supported by the authors' own prior review rather than by the independent primary studies the paper's rubric requires.

  1. self citation load bearing [Section 1.2; Table 1 rows C5–C7; Section 5.1]
    "The MARL algorithm and application base on which it rests was established in our earlier review of MARL applications in smart factories [Bahrpeyma and Reichelt, 2022], and is treated here as given; the present paper asks how language model components can improve, surround, train, or potentially displace parts of that base."

    Table 1 rates MARL as P:S for closed-loop task learning (C5), cooperative coordination (C6), and credit assignment (C7), which are the rows that carry the Section 5.1 assignment of Layer 2 to MARL. Section 1.4 defines S as requiring convergent evidence from at least two independent primary studies or benchmark families. In Table 1, C7 cites only [Bahrpeyma and Reichelt, 2022]; C5 and C6 cite LLM papers plus the same self-review, and none of these citations supplies the required independent MARL primary evidence. Appendix A covers only nine readiness-corpus works and explicitly says it is not a mechanical derivation of every rating.

full rationale

The paper's three-layer architecture is mostly derived from an explicit, scoped capability profile and a large external bibliography, and the LLM-Augmented Dec-POMDP is explicitly descriptive, so no circularity arises from the notation itself. The principal circularity burden is the self-citation chain behind the MARL S ratings in Table 1. Section 1.4 defines S as requiring convergent evidence from at least two independent primary studies or benchmark families, but the decisive MARL rows C5–C7 cite the authors' own prior review [Bahrpeyma and Reichelt, 2022] as the representative evidence, with C7 citing only that review. Section 1.2 explicitly treats the MARL base as established in that earlier self-authored review and as given for the present paper, making the evidence for the Layer 2 MARL default inherited rather than demonstrated. This is load-bearing because the central conditional claim in Section 5.1—that Layer 2 is assigned to MARL as a default derived from current evidence—depends on those ratings. The paper's own limitation statements, acknowledging the profile is an authorial synthesis without inter-rater reliability and prescribing independent two-coder recoding before treating it as a reproducible instrument, strengthen this concern. Still, much of the paper's contribution is independent: the taxonomy, the four attachment points, the decision framework, the reporting checklist, the readiness assessment, and the large set of external LLM and MARL references. The central claim is therefore partially, not wholly, circular; it does not reduce entirely to the self-citation or to definition.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The central argument rests on the literature corpus and on the proposed design rules, not on fitted parameters. The main axioms are: the Dec-POMDP/CTDE formalism matches the six manufacturing demands; the corpus is representative; the authors' prior MARL review is reliable; the evidence-rating thresholds are meaningful; and the five-level readiness scale is a valid ordinal measurement. The paper discloses the last two as unvalidated, which is why they are listed as ad hoc to this paper.

assumptions (5)
  • domain assumption The six manufacturing demands are jointly best captured by a Dec-POMDP/CTDE formulation for cooperative MARL.
    Used throughout (Sections 1.1 and 2.1) to justify MARL as analytical baseline; if another formalism were more appropriate, the Layer 2 assignment would lose its foundation.
  • domain assumption The literature corpus and inclusion criteria of Section 1.4 are sufficient and representative for the capability profile.
    The review is not PRISMA-complete and is based on iterative search; the authors disclose this. The conclusions are bounded by the evidence, but the architecture's grounding depends on corpus breadth.
  • ad hoc to paper The prior review by the same authors (Bahrpeyma and Reichelt, 2022) is treated as a reliable established evidence base for MARL applications and algorithms.
    Cited as given in Section 1.3 and used as representative evidence for MARL capabilities in Table 1 (C5-C7). This is a self-citation that supplies part of the support for the conditional MARL default.
  • ad hoc to paper The decision rules for evidence ratings (S requires two independent studies, etc.) are appropriate for ordinal synthesis.
    Defined in Section 1.4; the thresholds are hand-chosen and not validated, though they are explicit.
  • ad hoc to paper The five-level readiness scale is a meaningful ordinal measure of deployment maturity.
    Proposed in Section 9.1; the authors note it has not been validated against practitioner judgments (Section 10).

how reviews work

0 comments
Cite this review

Pith. "Pith review of A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing." pith.science (2026). https://pith.science/paper/QAV2KKAK

@misc{pith2026260807148,
  author       = {Pith},
  title        = {Pith review of: A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QAV2KKAK}},
  note         = {Machine review of arXiv:2608.07148}
}
read the original abstract

Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity, reflex speed response with long horizon effects, delayed and diffuse outcomes, and dynamics that resist explicit modeling. Cooperative multiagent reinforcement learning (MARL), posed as a Dec-POMDP under centralized training with decentralized execution, is a particularly natural formalism for these demands. This paper adopts a MARL centered scope and asks where large language models (LLMs) should augment, interface with, train, or, in the strongest competitive case, replace that coordination core. A taxonomy organizes the literature through four LLM attachment points: policy, reward design, communication between agents, and hierarchical planning. A conditional capability profile separates native mechanism, reported performance, formal guarantee, and engineering maturity, and a deployment readiness analysis identifies the evidence behind each role. These stages yield the principal contribution: a three layer MARL centered reference architecture, grounded in evidence, for semantic reasoning, adaptive cooperative control, and independently assured execution. The LLM-Augmented Dec-POMDP is a descriptive comparative notation for that architecture, recording four attachment choices without introducing a new decision process class or algorithm. Under the reviewed evidence, conventional MARL is better suited to frequent, structured, decentralized coordination after task specific training, whereas LLM components are promising for semantic interpretation, reward drafting, human interaction, and slower supervisory planning. Current LLM only manufacturing controllers do not yet establish equivalence for strict real time, decentralized, safety critical control; this conclusion is bounded by the available evidence and does not assert impossibility.

Figures

Figures reproduced from arXiv: 2608.07148 by the authors.

Figure 1
Figure 1. Evolution of manufacturing intelligence from fixed rules to LLM augmented multiagent [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. A graphical taxonomy of LLM attachment points to a MARL formulated Dec-POMDP, [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. A capability comparison distilled from Table [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The proposed, testable reference architecture for LLM and MARL systems. Layer [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: A decision flowchart for attaching an LLM to a MARL centered manufacturing controller, [PITH_FULL_IMAGE:figures/full_fig_p024_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 14 canonical work pages

  1. [3]

    Ajay Bandi, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor, and Sri Vidya Vilipala

    doi: 10.1038/s41598-026-54971-6. Ajay Bandi, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor, and Sri Vidya Vilipala. The rise of agentic ai: A review of definitions, frameworks, architectures, applications, evaluation metrics, and challenges.Future Internet, 17(9):404,

    work page doi:10.1038/s41598-026-54971-6

  2. [10]

    Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang

    arXiv:2410.14383, preprint. Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang. Safe multi-agent reinforcement learning for multi-robot control.Artificial Intelligence, 319: 103905,

    arXiv

  3. [11]

    Embodied llm agents learn to cooperate in organized teams.arXiv preprint arXiv:2403.12482,

    Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Velez, Qingyun Wu, Huazheng Wang, Thomas L Griffiths, and Mengdi Wang. Embodied llm agents learn to cooperate in organized teams.arXiv preprint arXiv:2403.12482,

    arXiv

  4. [12]

    arXiv:2502.14496

    doi: 10.48550/arXiv.2502.14496. arXiv:2502.14496. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, et al. Metagpt: Meta programming for multi-agent collaborative framework. InThe Twelfth International Conference on Learning Representations,

    arXiv doi:10.48550/arxiv.2502.14496

  5. [13]

    How far are we on the decision-making of llms? evaluating llms gaming ability in multi-agent environments.arXiv preprint arXiv:2403.11807,

    Jen-tse Huang, Eric John Li, Man Ho Lam, Tian Liang, Wenxuan Wang, Youliang Yuan, Wenxiang Jiao, Xing Wang, Zhaopeng Tu, and Michael R Lyu. How far are we on the decision-making of llms? evaluating llms gaming ability in multi-agent environments.arXiv preprint arXiv:2403.11807,

    arXiv

  6. [14]

    Dazzle Johnson, Gang Chen, and Yuqian Lu

    arXiv:2411.10184. Dazzle Johnson, Gang Chen, and Yuqian Lu. Multi-agent reinforcement learning for real-time dynamic production scheduling in a robot assembly cell.IEEE Robotics and Automation Letters,

    arXiv

  7. [16]

    TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models

    Haechang Kim, Hao Chen, Can Li, and Jong Min Lee. Talktoagent: A human-centric explanation of reinforcement learning agents with large language models.arXiv preprint arXiv:2509.04809,

    work page Pith review arXiv

  8. [18]

    Camel: Communicative agents for mind exploration of large language model society.Advances in Neural Information Processing Systems, 36, 2023a

    Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for mind exploration of large language model society.Advances in Neural Information Processing Systems, 36, 2023a. Haoran Li, Ziyi Su, Yun Xue, Zhiliang Tian, Yiping Song, and Minlie Huang. Advancing collaborative debates with role diffe...

Show all 45 references
  1. [19]

    Zhemin Li, Ruobing Zhang, Zhengming Wang, Zheng Xie, and Yiping Song

    doi: 10.1007/s10514-026-10257-4. Zhemin Li, Ruobing Zhang, Zhengming Wang, Zheng Xie, and Yiping Song. LLM-guided decision- making toolkit for multi-agent reinforcement learning.Neurocomputing, 638:130105, 2025b. doi: 10.1016/j.neucom.2025.130105. Xiaoyu Liang et al. Multi-age...

  2. [20]

    Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang

    doi: 10.1609/aaai.v40i38.40487. Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization.arXiv preprint arXiv:2310.02170,

    arXiv

  3. [21]

    arXiv:2507.14995, early access

    doi: 10.1109/TSG.2026.3684885. arXiv:2507.14995, early access. Derrick Low and Ye Zhou. Cooperative multi-agent reinforcement learning for robotic systems: A review.Multiagent and Grid Systems, 21(2),

    2026

  4. [22]

    v40i35.40201

    doi: 10.1609/aaai. v40i35.40201. Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models.arXiv preprint arXiv:2310.12931,

    arXiv

  5. [26]

    A survey of continual reinforcement learning.arXiv preprint arXiv:2506.21872, 2025a

    Chaofan Pan, Xin Yang, Yanhua Li, Wei Wei, Tianrui Li, Bo An, and Jiye Liang. A survey of continual reinforcement learning.arXiv preprint arXiv:2506.21872, 2025a. Guanzhong Pan, Vishal Chodnekar, Abinas Roy, and Haibo Wang. A cost-benefit analysis of on-premise large language ...

    arXiv

  6. [27]

    In-Beom Park, Jaeseok Huh, Joongkyun Kim, and Jonghun Park

    doi: 10.18653/v1/2025.acl-long.1459. In-Beom Park, Jaeseok Huh, Joongkyun Kim, and Jonghun Park. A reinforcement learning approach to robust scheduling of semiconductor manufacturing facilities.IEEE Transactions on Automation Science and Engineering, 17(3):1420–1431,

    2025 doi

  7. [28]

    Refiner: Reasoning feedback on intermediate representations.arXiv preprint arXiv:2304.01904,

    Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. Refiner: Reasoning feedback on intermediate representations.arXiv preprint arXiv:2304.01904,

    arXiv

  8. [29]

    Adapt: As-needed decomposition and planning with language models.arXiv preprint arXiv:2311.05772,

    Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, and Tushar Khot. Adapt: As-needed decomposition and planning with language models.arXiv preprint arXiv:2311.05772,

    arXiv

  9. [30]

    A negotiation-based multi-agent reinforcement learn- ing approach for dynamic scheduling of reconfigurable manufacturing systems.arXiv preprint arXiv:2511.07707,

    Manonmani Sekar and Nasim Nezamoddini. A negotiation-based multi-agent reinforcement learn- ing approach for dynamic scheduling of reconfigurable manufacturing systems.arXiv preprint arXiv:2511.07707,

  10. [31]

    Retrieval-augmented generation: A comprehensive survey of architectures, enhancements, and robustness frontiers.arXiv preprint arXiv:2506.00054,

    Chaitanya Sharma. Retrieval-augmented generation: A comprehensive survey of architectures, enhancements, and robustness frontiers.arXiv preprint arXiv:2506.00054,

    arXiv

  11. [32]

    Llm-based multi-agent reinforcement learning: Current and future directions.arXiv preprint arXiv:2405.11106,

    Chuanneng Sun, Songjun Huang, and Dario Pompili. Llm-based multi-agent reinforcement learning: Current and future directions.arXiv preprint arXiv:2405.11106,

    arXiv

  12. [33]

    Value-decomposition networks for cooperative multi-agent learning based on team reward

    Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech M Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al. Value-decomposition networks for cooperative multi-agent learning based on team reward. InProceedings of the 17t...

  13. [34]

    Tristan Tomilin, Luka van den Boogaard, Samuel Garcin, Constantin Ruhdorfer, Bram Grooten, Andreas Bulling, Mykola Pechenizkiy, and Meng Fang

    arXiv:2509.23537, preprint. Tristan Tomilin, Luka van den Boogaard, Samuel Garcin, Constantin Ruhdorfer, Bram Grooten, Andreas Bulling, Mykola Pechenizkiy, and Meng Fang. Meal: A benchmark for continual multi- agent reinforcement learning.arXiv preprint arXiv:2506.14990,

  14. [35]

    Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang

    doi: 10.3233/FAIA251260. Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang. Qplex: Duplex dueling multi-agent q-learning. InInternational Conference on Learning Representations,

    doi

  15. [36]

    Real-time decision support with reinforcement learning for dynamic flowshop scheduling

    Jinzhi Wang, Shuhui Qu, Jie Wang, James O Leckie, and Rui Xu. Real-time decision support with reinforcement learning for dynamic flowshop scheduling. InSmart SysTech 2017; European Conference on Smart Objects, Systems and Technologies, pages 1–9, Munich, Germany, June

    2017

  16. [37]

    Autogen: Enabling next-gen llm applications via multi-agent conversation framework.arXiv preprint arXiv:2308.08155,

    43 Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation framework.arXiv preprint arXiv:2308.08155,

    arXiv

  17. [38]

    Text2reward: Reward shaping with language models for reinforcement learning.arXiv preprint arXiv:2309.11489,

    Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu. Text2reward: Reward shaping with language models for reinforcement learning.arXiv preprint arXiv:2309.11489,

    arXiv

  18. [39]

    Llm- upgraded graph reinforcement learning for carbon-aware job scheduling in smart manufacturing

    Zhiying Yang, Fang Liu, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low, and Boon Ping Gan. Llm- upgraded graph reinforcement learning for carbon-aware job scheduling in smart manufacturing. arXiv preprint arXiv:2512.06351,

  19. [40]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao

    arXiv:2604.00722, preprint, publication status unconfirmed. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InThe Eleventh International Conference on Learning Representations,

  20. [41]

    Co-navgpt: Multi-robot cooperative visual semantic navigation using large language models.arXiv preprint arXiv:2310.07937,

    Bangguo Yu, Hamidreza Kasaei, and Ming Cao. Co-navgpt: Multi-robot cooperative visual semantic navigation using large language models.arXiv preprint arXiv:2310.07937,

    arXiv

  21. [42]

    Multiagent continual coordination via progressive task contextualization.arXiv preprint arXiv:2305.13539,

    Lei Yuan, Lihe Li, Ziqian Zhang, Fuxiang Zhang, Cong Guan, and Yang Yu. Multiagent continual coordination via progressive task contextualization.arXiv preprint arXiv:2305.13539,

    arXiv

  22. [43]

    Collmlight: Cooperative large language model agents for network-wide traffic signal control.arXiv preprint arXiv:2503.11739,

    Zirui Yuan, Siqi Lai, and Hao Liu. Collmlight: Cooperative large language model agents for network-wide traffic signal control.arXiv preprint arXiv:2503.11739,

    arXiv

  23. [44]

    Yuan Zhuang, Yi Shen, Zhili Zhang, Yuxiao Chen, and Fei Miao

    doi: 10.1109/LRA.2025.3577527. Yuan Zhuang, Yi Shen, Zhili Zhang, Yuxiao Chen, and Fei Miao. YOLO-MARL: You only LLM once for multi-agent reinforcement learning. InProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),

    2025

  24. [45]

    arXiv:2410.03997

    doi: 10.48550/arXiv.2410.03997. arXiv:2410.03997. Jonas Zinn, Birgit Vogel-Heuser, and Marius Gruber. Fault-tolerant control of programmable logic controller-based production systems with deep reinforcement learning.Journal of Mechanical Design, 143(7),

    doi arXiv

  25. [1998]

    Smart-llm: Smart multi-agent robot task planning using large language models.arXiv preprint arXiv:2309.10062,

    Shyam Sundar Kannan, Vishnunandan L N Venkatesh, and Byung-Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models.arXiv preprint arXiv:2309.10062,

    arXiv

  26. [2010]

    Reflecsched: Solving dynamic flexible job-shop scheduling via llm-powered hierarchical reflection.arXiv preprint arXiv:2508.01724,

    Shijie Cao and Yuan Yuan. Reflecsched: Solving dynamic flexible job-shop scheduling via llm-powered hierarchical reflection.arXiv preprint arXiv:2508.01724,

  27. [2015]

    Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. InInternational conference on machine learning, pages 1928–1937, New York, NY, USA, June

    1928

  28. [2016]

    Rithesh Murthy, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Le Xue, Weiran Yao, Yihao Feng, Zeyuan Chen, Akash Gokul, Devansh Arpit, et al

    PMLR. Rithesh Murthy, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Le Xue, Weiran Yao, Yihao Feng, Zeyuan Chen, Akash Gokul, Devansh Arpit, et al. Rex: Rapid exploration and exploitation for ai agents.arXiv preprint arXiv:2307.08962,

    arXiv

  29. [2017]

    Multi-agent consensus seeking via large language models.arXiv preprint arXiv:2310.20151,

    Huaben Chen, Wenkang Ji, Lufeng Xu, and Sen Zhao. Multi-agent consensus seeking via large language models.arXiv preprint arXiv:2310.20151,

    arXiv

  30. [2019]

    Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734,

    Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, et al. Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734,

    arXiv

  31. [2020]

    Roco: Dialectic multi-robot collaboration with large language models.arXiv preprint arXiv:2307.04738,

    Zhao Mandi, Shreeya Jain, and Shuran Song. Roco: Dialectic multi-robot collaboration with large language models.arXiv preprint arXiv:2307.04738,

    arXiv

  32. [2021]

    doi: https://doi.org/10.1016/B978-0-12-815073-3.00012-0

    ISBN 978-0-12-815073-3. doi: https://doi.org/10.1016/B978-0-12-815073-3.00012-0. Aleksandar Krnjaic, Raul D Steleac, Jonathan D Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To, Kuan-Ho Lao, Murat Cubuktepe, Matthew Haley, Peter Börsting, and Stefano V Albrecht...

    arXiv

  33. [2022]

    2022.1027340

    doi: 10.3389/frobt. 2022.1027340. Jinyin Bai, Wei Zhu, Xiangchen Wang, KaiYang Kou, Shiluo Guo, Shuhong Liu, Dong Li, Tianjin Ni, Jinji Zhou, and Yihao Zhong. A hierarchical multi-agent reinforcement learning framework with high-level guidance from large language models.Scient...

    2022

  34. [2023]

    BerendDenkena, Marc-AndréDittrich, SilasFohlmeister, DanielKemp, andGregoryPalmer. Scalable cooperative multi-agent-reinforcement-learning for order-controlled on schedule manufacturing in flexible manufacturing systems.Simulation in Produktion und Logistik 2021: Erlangen, 15....

    2021

  35. [2024]

    Hong Chen

    arXiv:2404.00282. Hong Chen. Theoretical foundations for cyber-physical systems: a literature review.Journal of Industrial Integration and Management, 2(03):1750013,

    arXiv

  36. [2025]

    Do as i can, not as i say: Grounding language in robotic affordances

    Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. Do as i can, not as i say: Grounding language in robotic affordances. InConference on Robot Learning, pages 287–318. PMLR, 2023a....

    arXiv

  37. [2026]

    arXiv:2605.18077

    doi: 10.48550/arXiv.2605.18077. arXiv:2605.18077. Fouad Bahrpeyma and Dirk Reichelt. A review of the applications of multi-agent reinforcement learning in smart factories.Frontiers in Robotics and AI, 9:1027340,

    doi arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.