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REVIEW 3 major objections 3 minor 40 references

How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective

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

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Pith's one-line read LLM-generated plans on the tested benchmarks average only 2.65 executable actions, and an NLP repair pipeline raises success from 21.9% to 27.5% while still trailing classical planners.

desk verdict A plausible abstract for an LLM-planning evaluation paper, but the record's full text is an unrelated optimization paper, so nothing here can be audited and the reasoning claim is untestable. read the letter →

arxiv 2508.01300 v1 pith:5QYZFDHE submitted 2025-08-02 cs.AI

Ma'ayan Armony , Albert Meroño-Peñuela , Gerard Canal This is my paper · ORCID
classification cs.AI
keywords largelanguagemodelsAIplanningplangenerationnaturalprocessingrepairsymbolicplannersexecutabilityevaluationLLMbenchmarking
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 asks how far large language models are from symbolic planners when planning is treated as a natural language task rather than a scoring exercise. It proposes a recovery pipeline that evaluates LLM-generated plans with NLP methods, repairs them through three text-manipulation stages, and completes them with a symbolic planner. On the benchmark tasks, only the first 2.65 actions of an average LLM-generated plan are executable, while symbolically generated plans average 8.4 actions. The pipeline improves action quality and raises the overall success rate from 21.9% to 27.5%, but it still falls short of classical planners in quality and reliability. The authors read this as no clear evidence of underlying reasoning during plan generation.

What carries the argument

The central mechanism is the recovery pipeline: an NLP-based evaluation stage that diagnoses the generated plan, three recovery stages that manipulate the plan as text, and a symbolic planner that completes the plan from the repaired output. The pipeline carries the argument by separating diagnosis of plan quality, handled linguistically, from the guarantee of correctness, handled symbolically, and the paper measures how much of the gap each component recovers.

What would settle it

Run the same executability check on a diverse benchmark set that varies task length, domain novelty, and prompt wording; if LLM plans in any such setting regularly show long executable prefixes approaching the symbolic plan length, the claim that observable behavior gives no evidence of underlying reasoning would be contradicted.

Watch

Extended reading notes

Core claim

The paper's central claim is that when LLM-generated plans are analyzed as natural language artifacts rather than simply scored right or wrong, observable behavior shows no clear evidence of underlying reasoning during plan generation. On average, only the first 2.65 actions of a generated plan are executable against the task's action semantics, while symbolic planners produce plans averaging 8.4 actions. An NLP-based recovery pipeline, which evaluates the plans in natural language, repairs them through three manipulation stages, and completes them with a symbolic planner, improves action quality and raises the overall success rate from 21.9% to 27.5%, yet still falls short of the quality and reliability of classical planners.

Load-bearing premise

The load-bearing premise is that the benchmark tasks, prompts, executability checker, and the three recovery stages are representative and fairly configured, so the 2.65-action average and the 21.9%-to-27.5% figures describe LLM planning ability generally rather than this particular setup.

Editorial extensions

If this is right

  • LLM-generated plans should be treated as drafts with a short trustworthy prefix, not as complete solutions.
  • Reporting success rate alone hides the failure pattern; executable prefix length and action quality should be reported alongside it.
  • NLP-based repair can recover a small but measurable share of failures, raising success from 21.9% to 27.5%.
  • A hybrid workflow, with an LLM for unstructured problem intake and a symbolic planner for sound completion, remains the more reliable path on these benchmarks.
  • The gap between the pipeline's 27.5% success and classical planner reliability defines the remaining work for LLM planning components.

Reading between the lines

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

  • A testable extension the paper leaves implicit is to measure whether executable prefix length grows with plan length; if it stays near two or three actions regardless of task size, that would support a local pattern-matching reading of LLM behavior.
  • The negative conclusion about reasoning is bounded by the tests used; a natural next experiment is to probe with tasks that cannot be solved by pattern matching, such as novel domain combinations, where a correct plan would force genuine inference.
  • The modest 5.6 percentage point gain from NLP repair suggests text-level manipulation can patch surface errors but not structural ones, so using the NLP evaluator as a heuristic to guide symbolic search may be more promising than repairing text directly.
  • If these numbers generalize, planning benchmarks should adopt executable prefix length as a standard interpretability metric alongside success rate, because it localizes where LLM planning fails.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The abstract claims an empirical study of LLM planning: the authors propose an NLP-based recovery pipeline with three stages that evaluates and repairs LLM-generated plans and completes them with a symbolic planner. They report that on average only the first 2.65 actions of a plan are executable, the average symbolic plan length is 8.4 actions, the pipeline increases overall success from 21.9% to 27.5%, and that the results reveal no clear evidence of underlying reasoning during plan generation. However, the full text supplied in the submission record is not this paper: it is arXiv:2508.01299v2, a manuscript on Frank-Wolfe heuristics for mixed-integer quadratically constrained quadratic programs, with no content in common with the abstract. Consequently, none of the experimental setup, benchmarks, prompts, executability checker, or recovery-stage details behind the reported numbers is available for inspection.

Significance. If the claimed results were properly documented, the paper would address a timely question about LLM planning ability and introduce a practically oriented repair pipeline with modest but positive gains. The quantitative findings (2.65 executable actions, 8.4-action average plan, 21.9% to 27.5% success improvement) would be a useful benchmark reference for the LLM planning community. Yet as submitted, the manuscript provides no verifiable evidence: the abstract is the only substantive content, and it is unsupported by any methods section, dataset description, or results table. The negative claim about reasoning is also not operationally defined. The paper therefore cannot be evaluated on its scientific merits in its current form.

major comments (3)
  1. [Full text] The full text provided in the submission record is a different manuscript (arXiv:2508.01299v2) on Frank-Wolfe solvers for mixed-integer quadratically constrained quadratic programs, with no overlap in topic, experiments, or results with the abstract; this is a load-bearing defect because none of the experimental claims in the abstract can be checked against the body of the paper.
  2. [Abstract, findings paragraph] The abstract never specifies what observable evidence would count as "underlying reasoning" during plan generation, so the central negative conclusion is not falsifiable from the reported data; a reader cannot tell which measurements would have led the authors to the opposite conclusion.
  3. [Abstract] The headline statistics are presented without any benchmark definition, LLM model or prompt details, plan-validation semantics, sample sizes, or confidence intervals; for example, "2.65 actions are executable" is uninterpretable without the executability checker's action semantics and the distribution of tasks over which the average is taken.
minor comments (3)
  1. [Abstract] The phrase "NLP-based analysis of the plans" and "NLP manipulation of the LLM-generated plans" would benefit from a concrete definition of the linguistic features or transformations used.
  2. [Abstract] The paper's title promises a comparison with symbolic planners, but the abstract does not report the success rate or plan length of the symbolic planner on the same benchmark instances, only stating that the pipeline "falls short" of it.
  3. [Abstract] The term "recovery pipeline" is introduced without naming the three stages; even a brief list in the abstract would help situate the contribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established: the abstract contains no derivation or equations, and the supplied full text is an unrelated MIQCQP paper, leaving no claimed derivation chain to audit.

full rationale

The target paper's abstract reports empirical measurements (2.65 executable actions, 8.4 average plan length, success rates 21.9% and 27.5%) and interpretive claims about LLM reasoning and a three-stage recovery pipeline. None of these claims are derived from equations or definitions, so the classic circularity patterns—self-definitional reductions, fitted inputs renamed as predictions, or uniqueness theorems imported from the authors' prior work—cannot be exhibited from the abstract alone. The supplied full text is arXiv:2508.01299v2, a completely different manuscript about Frank-Wolfe solvers for MIQCQPs by different authors; it shares no content with the abstract. There is therefore no derivation chain, no benchmark definition, no executability checker specification, and no description of how the recovery stages were configured. While this mismatch is a serious verifiability defect—the reported numbers cannot be checked, and the negative reasoning claim is not falsifiable as stated—it is not evidence of circularity. Per the hard rules, circularity requires quoting the paper and exhibiting a specific reduction, and no such reduction is available. The possibility that the recovery pipeline was tuned on the same benchmarks used for evaluation is speculative without the missing methodology, and speculation is explicitly disallowed. Hence the honest finding is no significant circularity, with score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

All entries are inferred from the abstract because the supplied full text is a different manuscript (arXiv 2508.01299, a MIQCQP solver paper). No free parameters are identifiable at abstract level; the three recovery stages are likely to contain thresholds or weights not disclosed in the abstract, which would be free parameters if tuned on the evaluation benchmarks. No invented entities in the sense of new physical or formal objects appear in the abstract.

assumptions (3)
  • domain assumption Success rate and action executability are valid and sufficient quality measures for comparing LLM plans with symbolic planner plans
    The central comparison (21.9% to 27.5% success, 2.65 executable actions) presumes these metrics are the right yardsticks; the abstract gives no external validation of the metrics.
  • domain assumption The benchmark planning tasks and LLM prompts used are representative of planning problems in general
    The general claim about LLM planning ability, the stated lack of evidence for underlying reasoning, generalizes only if the task sample is representative; the abstract does not describe the task distribution.
  • domain assumption The symbolic planner's output provides correct ground truth for evaluating the LLM's plan prefix
    The pipeline completes plans with a symbolic planner and treats the result as the reference; an incorrect or incompatible planner translation would distort the executability numbers.

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Cite this review

Pith. "Pith review of How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective." pith.science (2026). https://pith.science/paper/5QYZFDHE

@misc{pith2026250801300,
  author       = {Pith},
  title        = {Pith review of: How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5QYZFDHE}},
  note         = {Machine review of arXiv:2508.01300}
}
read the original abstract

The reasoning and planning abilities of Large Language Models (LLMs) have been a frequent topic of discussion in recent years. Their ability to take unstructured planning problems as input has made LLMs' integration into AI planning an area of interest. Nevertheless, LLMs are still not reliable as planners, with the generated plans often containing mistaken or hallucinated actions. Existing benchmarking and evaluation methods investigate planning with LLMs, focusing primarily on success rate as a quality indicator in various planning tasks, such as validating plans or planning in relaxed conditions. In this paper, we approach planning with LLMs as a natural language processing (NLP) task, given that LLMs are NLP models themselves. We propose a recovery pipeline consisting of an NLP-based evaluation of the generated plans, along with three stages to recover the plans through NLP manipulation of the LLM-generated plans, and eventually complete the plan using a symbolic planner. This pipeline provides a holistic analysis of LLM capabilities in the context of AI task planning, enabling a broader understanding of the quality of invalid plans. Our findings reveal no clear evidence of underlying reasoning during plan generation, and that a pipeline comprising an NLP-based analysis of the plans, followed by a recovery mechanism, still falls short of the quality and reliability of classical planners. On average, only the first 2.65 actions of the plan are executable, with the average length of symbolically generated plans being 8.4 actions. The pipeline still improves action quality and increases the overall success rate from 21.9% to 27.5%.

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Reference graph

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