{"id":"8f4b7255-fe86-4e82-bb5d-d85b5dc45a24","arxiv_id":"2606.03872","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A neurosymbolic method uses LLMs to filter feasible deviation transitions in discrete-event systems and computes symbolic robustness guarantees, shown on three case studies to yield smaller deviation sets with comparable guarantees.","lead":"The paper proposes a neurosymbolic framework that uses large language models to infer feasible extra transitions in discrete-event system models and then applies symbolic computation to determine robustness guarantees for safety specifications. A smart generalist might read it to understand how AI can make formal verification methods more practical for real control systems that face modeling errors or faults.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"LLM inference accuracy for feasible deviations remains unverified and central to comparable guarantees","rationale":"The reader's weakest assumption matches the load-bearing point exactly. The neurosymbolic split makes the overall guarantee only as strong as the neural inference; without independent validation of that inference the claim cannot be accepted at face value. The UNVERDICTED verdict with low confidence is therefore appropriate and requires no adjustment.","tokens_in":1711,"tokens_out":310,"duration_ms":18005,"concrete_test":"For each of the three case studies, obtain an exhaustive manual enumeration of feasible deviation transitions by domain experts using only the plant model, specification, and stated domain knowledge; compare this ground-truth set against the LLM output for both omissions and inclusions. If any omission occurs that alters the robustness verdict, the preservation claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that the neural layer produces a set S_feas of feasible deviations such that the symbolic robustness computation over S_feas yields a result comparable to the full (infeasible-inclusive) transition set. This holds only if the LLM neither omits feasible transitions that would falsify the specification nor adds infeasible ones that would artificially shrink the reported set. No soundness, completeness, or error-bound argument is supplied for the LLM step; the symbolic layer's guarantees are therefore conditional on an unproven inference step whose failure mode directly invalidates the \"preserving robustness guarantees\" part of the claim.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","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.","tokens_in":1861,"tokens_out":470,"duration_ms":18194,"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":[{"comment":"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.","section":"Abstract"},{"comment":"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.","section":"Framework description"}],"minor_comments":[{"comment":"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.","section":"Framework description"}],"recommendation":"major_revision","confidential_remarks":"The manuscript presents an interesting idea but the absence of any quantitative validation or LLM error analysis makes it difficult to assess readiness for a systems journal; the work reads as preliminary."},"author_rebuttal":{"model":"grok-4.3","summary":"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.","responses":[{"response":"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_made":"yes","referee_comment":"[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."},{"response":"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_made":"partial","referee_comment":"[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."}],"tokens_in":1348,"tokens_out":479,"duration_ms":21777,"standing_objections":["No formal soundness, completeness, or error-bound argument can be supplied for the LLM inference step, as large language models are not formally verifiable."]},"desk_editor":{"model":"grok-4.3","letter":"The main thing here is a two-layer setup: an LLM guesses which extra transitions are feasible given the model and specs, then a symbolic checker computes the robust sets only over those. That combination is new for this subfield and directly targets the scalability and conservatism problems in transition-deviation robustness.\n\nWhat the paper does well is lay out a clean procedural split. The symbolic layer keeps the formal guarantees once the deviation set is fixed, and the motivation for pruning is stated plainly. The three case studies are mentioned as showing smaller feasible sets with comparable results, which at least points in the right direction.\n\nThe soft spot is obvious and load-bearing: nothing is shown about whether the LLM inference is accurate. No metrics on missed or extra transitions, no error analysis, no comparison to ground truth. The claim that guarantees are preserved therefore rests on an untested assumption. If the LLM drops a critical feasible deviation or adds an impossible one, the symbolic output is either unsafe or overly optimistic. The abstract does not supply any argument or experiment that closes this gap.\n\nThis is the kind of paper that would interest people working on discrete-event control who already use symbolic tools and are looking for ways to cut down the search space. A reader who wants a fully verified pipeline will find the current version thin. I would not send it to referees in this state; the empirical gap on the neural layer needs to be filled first.","headline":"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.","tokens_in":2362,"tokens_out":361,"would_cite":false,"duration_ms":11013,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A neurosymbolic framework uses LLMs to infer feasible deviation transitions and then applies symbolic computation to obtain discrete robustness guarantees over the reduced set.","keywords":["neurosymbolic computing","discrete-event systems","robustness analysis","supervisory control","large language models","transition deviations","safety properties"],"falsifier":"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.","tokens_in":2603,"feed_emoji":"","tokens_out":586,"duration_ms":17517,"temperature":0.7,"pith_summary":"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.","feed_headline":"Neurosymbolic filter cuts deviation sets in discrete robustness checks","feed_subtitle":"LLM infers feasible extra transitions so symbolic analysis runs on a smaller set without losing safety guarantees.","key_machinery":"The two-layer neurosymbolic framework, where the neural layer infers feasible deviation transitions and the symbolic layer performs the robustness computation.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["LLM infers feasible deviations for symbolic discrete robustness","Neurosymbolic robustness over LLM feasible transition set","Smaller deviation sets via neural layer in discrete analysis","Symbolic robustness analysis on neural inferred deviations","Discrete robustness guarantees preserved with reduced deviations"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["LLM infers feasible deviations for symbolic discrete robustness","Neurosymbolic robustness over LLM feasible transition set","Smaller deviation sets via neural layer in discrete analysis","Symbolic robustness analysis on neural inferred deviations","Discrete robustness guarantees preserved with reduced deviations"]},"model":"grok-4.3","cost_usd":0.006891,"raw_usage":{"total_tokens":3169,"prompt_tokens":610,"num_sources_used":0,"completion_tokens":68,"cost_in_usd_ticks":68912000,"prompt_tokens_details":{"text_tokens":610,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2491,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":610,"tokens_out":68,"duration_ms":17092,"temperature":1.0,"reasoning_tokens":2491,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T08:57:06.779633+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"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.","supporting_citations":[],"review_version":1}