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REVIEW 2 major objections 1 minor 27 references

NeuroSymbolic Robustness Analysis for Discrete Systems with Respect to Transition Deviations

T0 review · 2 major / 1 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read A neurosymbolic framework uses LLMs to infer feasible deviation transitions and then applies symbolic computation to obtain discrete robustness guarantees over the reduced set.

desk verdict The neurosymbolic pruning idea for discrete robustness is a sensible direction but the paper gives no evidence that the LLM step actually works without breaking the guarantees. read the letter →

arxiv 2606.03872 v1 pith:KFEP5DAG submitted 2026-06-02 eess.SY cs.SY

Shih-Jie Shih , Jonghan Lim , Ilya Kovalenko , Rômulo Meira-Góes This is my paper
classification eess.SYcs.SY
keywords neurosymboliccomputingdiscrete-eventsystemsrobustnessanalysissupervisorycontrollargelanguagemodelstransitiondeviationssafetyproperties
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

This paper proposes a neurosymbolic framework to analyze robustness of supervised discrete-event systems against model deviations modeled as extra transitions. The approach uses a neural layer with large language models to infer a reduced set of feasible deviations from the system model, specification, and domain knowledge. A symbolic layer then computes the robustness guarantees over only those inferred deviations. The goal is to improve scalability over exhaustive analysis of all possible extra transitions and reduce conservatism by excluding infeasible deviations. Evaluation on three case studies shows the method finds smaller feasible sets with robustness guarantees comparable to full analysis.

What carries the argument

The two-layer neurosymbolic framework, where the neural layer infers feasible deviation transitions and the symbolic layer performs the robustness computation.

What would settle it

A case where running the full transition-based analysis reveals either a feasible deviation missed by the LLM that breaks the guarantee or an infeasible one included by the LLM that changes the robustness conclusion.

Watch

Extended reading notes

Core claim

The central claim is that a neural reasoning layer based on Large Language Models can infer a set of feasible deviation transitions, allowing a subsequent symbolic layer to compute discrete robustness guarantees over this smaller set while achieving results comparable to analyzing the full set of possible transitions.

Load-bearing premise

The large language model accurately infers exactly the feasible deviation transitions without omitting any that could violate the specification or including any that are impossible.

Editorial extensions

If this is right

  • The supervised system maintains desired specifications under the inferred feasible deviations.
  • The method scales better by reducing the solution space of possible deviations.
  • Robustness guarantees are preserved compared to full transition-based analysis.
  • The framework applies to safety properties in discrete-event systems.

Reading between the lines

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

  • If the LLM inference generalizes well, this could enable robustness checks for much larger discrete systems where full enumeration is intractable.
  • Similar neurosymbolic splitting might apply to other formal verification tasks involving large search spaces of possible faults or deviations.
  • The approach opens the possibility of incorporating real-time data or simulation to refine the feasible deviation set dynamically.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The paper proposes a neurosymbolic framework for discrete robustness analysis in supervisory control of discrete-event systems. A neural layer based on large language models infers a set of feasible deviation transitions from system models, specifications, and domain knowledge; a subsequent symbolic layer then computes the discrete robustness guarantees over this reduced set. The central claim is that the method identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those obtained from full transition-based analysis, as demonstrated via evaluation on three case studies.

Significance. If the LLM inference step reliably identifies feasible deviations without critical omissions or invalid inclusions, the framework would address key limitations of existing discrete robustness notions by improving scalability and reducing conservatism. The neurosymbolic combination is a novel procedural approach for this domain and could enable more practical application of formal robustness analysis when the empirical validation is strengthened.

major comments (2)
  1. [Abstract] Abstract: the claim that the method 'identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition-based analysis' rests on evaluation of three case studies, yet the abstract (and manuscript) supplies no quantitative metrics, no description of how LLM accuracy or completeness was measured, and no error analysis. This leaves the load-bearing 'comparable guarantees' assertion without empirical support.
  2. [Framework description] Framework description (neural reasoning layer): no soundness, completeness, or error-bound argument is supplied for the LLM inference of feasible deviation transitions. The symbolic layer's guarantees are therefore conditional on an unverified step whose failure modes (omitting feasible transitions or adding infeasible ones) directly invalidate the central claim of preserved robustness.
minor comments (1)
  1. [Framework description] Notation for the inferred feasible set S_feas and its relation to the full transition set should be defined explicitly with an equation or diagram in the framework section to improve clarity.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments, which identify key areas where the manuscript can be strengthened. We address each major comment below, indicating where revisions will be made and where inherent limitations of the approach prevent formal guarantees.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the method 'identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition-based analysis' rests on evaluation of three case studies, yet the abstract (and manuscript) supplies no quantitative metrics, no description of how LLM accuracy or completeness was measured, and no error analysis. This leaves the load-bearing 'comparable guarantees' assertion without empirical support.

    Authors: We agree that the manuscript does not currently include quantitative metrics, a description of LLM accuracy/completeness measurement, or error analysis. In the revised version we will expand the evaluation section (and update the abstract) to report concrete metrics from the three case studies, including the percentage reduction in deviation-set size, precision/recall of inferred transitions against expert-validated ground truth where available, and direct numerical comparison of the resulting robustness sets against the full-transition baseline. revision: yes

  2. Referee: [Framework description] Framework description (neural reasoning layer): no soundness, completeness, or error-bound argument is supplied for the LLM inference of feasible deviation transitions. The symbolic layer's guarantees are therefore conditional on an unverified step whose failure modes (omitting feasible transitions or adding infeasible ones) directly invalidate the central claim of preserved robustness.

    Authors: The manuscript makes no claim of formal soundness or completeness for the LLM inference step; the symbolic layer supplies exact robustness only with respect to the deviation set that the LLM produces. The central claim is therefore one of empirical comparability on the evaluated case studies rather than formal preservation. We will add an explicit limitations subsection that enumerates the possible failure modes of the neural layer and describes how the case-study results provide practical evidence that these modes did not materially affect the reported robustness sets. revision: partial

standing simulated objections not resolved
  • No formal soundness, completeness, or error-bound argument can be supplied for the LLM inference step, as large language models are not formally verifiable.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: neurosymbolic framework is a procedural combination without self-referential reductions

full rationale

The paper proposes a two-layer neurosymbolic method: an LLM-based neural layer infers a feasible deviation set from models/specs/domain knowledge, followed by a symbolic layer that computes robustness guarantees over that set. No equations, definitions, or self-citations are shown that reduce any output (e.g., the reported smaller feasible set or comparable guarantees) to an input quantity by construction. The approach is presented as a new combination addressing scalability and conservatism in prior discrete robustness notions; the symbolic guarantees are conditional on the neural inference step but do not collapse into it definitionally or via fitted parameters. This is a standard case of an independent procedural method evaluated on case studies, with no load-bearing self-citation chains or ansatzes smuggled in.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim rests on the unverified reliability of LLM inference for feasible deviations and on standard background results from supervisory control theory; no new physical entities are postulated and no numerical parameters are fitted inside the abstract.

assumptions (2)
  • domain assumption The plant model and specification are given as finite automata or similar discrete structures whose transition relation can be augmented by extra transitions.
    Invoked when the abstract defines deviations as additional transitions added to the plant.
  • ad hoc to paper Large language models can perform reliable inference of feasible transitions when supplied with system models, specifications, and domain knowledge.
    This is the load-bearing premise of the neural reasoning layer; it is not a standard result in the discrete-event systems literature.

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Cite this review

Pith. "Pith review of NeuroSymbolic Robustness Analysis for Discrete Systems with Respect to Transition Deviations." pith.science (2026). https://pith.science/paper/KFEP5DAG

@misc{pith2026260603872,
  author       = {Pith},
  title        = {Pith review of: NeuroSymbolic Robustness Analysis for Discrete Systems with Respect to Transition Deviations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KFEP5DAG}},
  note         = {Machine review of arXiv:2606.03872}
}
read the original abstract

Supervisory control of discrete-event systems provides formal guarantees of correctness with respect to a plant model and specification. However, these guarantees heavily rely on the plant model, which could deviate from nominal behavior due to modeling errors or faults. Recent notions of discrete robustness model deviations as a set of additional transitions that are added to the plant. The discrete robustness is defined as all sets of extra transitions for which the supervised system still guarantees a desired specification. However, this notion suffers from scalability due to the large solution space and conservatism since most deviations are infeasible in practice. This paper proposes to address these two issues using a neurosymbolic computing framework for discrete robustness analysis of safety properties. First, a neural reasoning layer based on Large Language Models infers a set of feasible deviation transitions from system models, specifications, and domain knowledge. Next, a symbolic layer computes the discrete robustness guarantees over the inferred deviation set. We evaluate our framework on three case studies, demonstrating that our method identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition-based analysis.

Figures

Figures reproduced from arXiv: 2606.03872 by the authors.

Figure 1
Figure 1. Models of plant and supervisor for manufacturing [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of our neurosymbolic approach to compute robustness. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Plant model for Therac-25. Transitions in black are [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (1 more)
Figure 3
Figure 3. Figure 3: A not robust deviated manufacturing plant. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]

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Works this paper leans on

27 extracted references · 3 canonical work pages

  1. [1]

    W. M. Wonham and K. Cai, Supervisory Control of Discrete- Event Systems. Springer International Publishing, 2018

    2018

  2. [2]

    C. G. Cassandras and S. Lafortune, Introduction to Discrete Event Systems, 3rd ed. Springer, Cham, 2021

    2021

  3. [3]

    Supervisory control of a class of discrete event processes,

    P. J. Ramadge and W. M. Wonham, “Supervisory control of a class of discrete event processes,” SIAM J. Control Optim., vol. 25, no. 1, pp. 206–230, Jan. 1987

    1987

  4. [4]

    On the synthesis of a reactive module,

    A. Pnueli and R. Rosner, “On the synthesis of a reactive module,” in Proceedings of the 16th ACM SIGPLAN-SIGACT Sympo- sium on Principles of Programming Languages, ser. POPL ’89. Association for Computing Machinery, 1989, p. 179–190

    1989

  5. [5]

    Supervi- sory control and reactive synthesis: a comparative introduction,

    R. Ehlers, S. Lafortune, S. Tripakis, and M. Y. Vardi, “Supervi- sory control and reactive synthesis: a comparative introduction,” Discrete Event Dynamic Systems, vol. 27, pp. 209–260, 2017

    2017

  6. [6]

    On tolerance of discrete systems with respect to transition perturbations,

    R. Meira-Góes, E. Kang, S. Lafortune, and S. Tripakis, “On tolerance of discrete systems with respect to transition perturbations,” Discrete Event Dynamic Systems, vol. 33, no. 4, pp. 395–424, 2023

    2023

  7. [7]

    Active fault tolerant control of discrete event systems using online diagnostics,

    A. Paoli, M. Sartini, and S. Lafortune, “Active fault tolerant control of discrete event systems using online diagnostics,” Automatica, vol. 47, no. 4, pp. 639–649, Apr. 2011

    2011

  8. [8]

    Bounded sensor failure tolerant supervisory control,

    K. Rohloff, “Bounded sensor failure tolerant supervisory control,” IF AC Proceedings Volumes, vol. 45, no. 29, pp. 272 – 277, 2012

    2012

Show all 27 references
  1. [9]

    Robust and adaptive supervisory control of discrete event systems,

    F. Lin, “Robust and adaptive supervisory control of discrete event systems,” IEEE Transactions on Automatic Control, vol. 38, no. 12, pp. 1848–1852, Dec 1993

    1993

  2. [10]

    Model uncertainty in discrete event systems,

    S. Young and V. K. Garg, “Model uncertainty in discrete event systems,” SIAM Journal on Control and Optimization, vol. 33, no. 1, pp. 208–226, 1995

    1995

  3. [11]

    Robustness of supervisors for discrete- event systems,

    J. Cury and B. Krogh, “Robustness of supervisors for discrete- event systems,” IEEE Transactions on Automatic Control, vol. 44, no. 2, pp. 376–379, 1999

    1999

  4. [12]

    Maximizing robustness of supervisors for partially observed discrete event systems,

    S. Takai, “Maximizing robustness of supervisors for partially observed discrete event systems,” Automatica, vol. 40, no. 3, pp. 531 – 535, 2004

    2004

  5. [13]

    Supervisor synthesis to thwart cyber attack with bounded sensor reading alterations,

    R. Su, “Supervisor synthesis to thwart cyber attack with bounded sensor reading alterations,” Automatica, vol. 94, pp. 35 – 44, 2018

    2018

  6. [14]

    Dealing with sensor and actuator deception attacks in supervisory control,

    R. Meira-Góes, H. Marchand, and S. Lafortune, “Dealing with sensor and actuator deception attacks in supervisory control,” Automatica, vol. 147, p. 110736, 2023

    2023

  7. [15]

    Robust supervisory control against intermittent loss of observations,

    M. V. S. Alves, J. C. Basilio, A. E. C. da Cunha, L. K. Carvalho, and M. V. Moreira, “Robust supervisory control against intermittent loss of observations,” IF AC Proceedings Volumes, vol. 47, no. 2, pp. 294 – 299, 2014

    2014

  8. [16]

    Control of networked discrete event systems: Dealing with communication delays and losses,

    F. Lin, “Control of networked discrete event systems: Dealing with communication delays and losses,” SIAM Journal on Control and Optimization, vol. 52, no. 2, pp. 1276–1298, 2014

    2014

  9. [17]

    Safe environmental envelopes of discrete systems,

    R. Meira-Góes, I. Dardik, E. Kang, S. Lafortune, and S. Tripakis, “Safe environmental envelopes of discrete systems,” in International Conference on Computer Aided Verification. Springer, 2023, pp. 326–350

    2023

  10. [18]

    Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning,

    A. Garcez, M. Gori, L. Lamb, L. Serafini, M. Spranger, and S. Tran, “Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning,” Journal of Applied Logics, vol. 6, no. 4, pp. 611–631, 2019

    2019

  11. [19]

    Baier and J.-P

    C. Baier and J.-P. Katoen, Principles of Model Checking. MIT Press, 2008

    2008

  12. [20]

    Sparks of artificial general intelligence: Early experiments with gpt-4,

    S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al., “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712, 2023

    2023 arXiv

  13. [21]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al., “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  14. [22]

    Bert: Pre-training of deep bidirectional transformers for language understanding,

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologi...

    2019

  15. [23]

    Mdesops: An open-source software tool for discrete event systems modeled by automata,

    R. Meira-Góes, A. Wintenberg, S. Matsui, and S. Lafortune, “Mdesops: An open-source software tool for discrete event systems modeled by automata,” in 22nd IF AC World Congress. Elsevier BV, 2023, pp. 6093–6098

    2023

  16. [24]

    An investigation of the Therac-25 accidents,

    N. Leveson and C. Turner, “An investigation of the Therac-25 accidents,” Computer, vol. 26, no. 7, pp. 18–41, 1993

    1993

  17. [25]

    Tel, Introduction to distributed algorithms

    G. Tel, Introduction to distributed algorithms. Cambridge university press, 2000

    2000

  18. [26]

    A behavioral notion of robustness for software systems,

    C. Zhang, D. Garlan, and E. Kang, “A behavioral notion of robustness for software systems,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020, pp. 1–12

    2020

  19. [27]

    Increasing the LLM accuracy for question answering: Ontologies to the rescue!

    D. Allemang and J. F. Sequeda, “Increasing the LLM accuracy for question answering: Ontologies to the rescue!” arXiv preprint arXiv:2405.11706, 2024

    2024

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