{"id":"4d95e96e-b3fa-4e37-9ba8-a9b897d943a9","arxiv_id":"2508.01300","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"LLM-generated plans are on average executable for only the first 2.65 of about 8.4 actions, and an NLP-based recovery pipeline plus symbolic completion raises success from 21.9% to 27.5%.","lead":"This paper treats LLM-produced plans as text that can be scored and repaired with natural language processing tools, then hands the repaired plan to a symbolic planner to finish. It reports that even after this recovery pipeline, LLM plans remain far below classical planners, with only the first 2.65 actions executable on average and success rising from 21.9% to 27.5%.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Full-text mismatch and an underspecified reasoning metric leave the abstract's quantitative and negative claims unverifiable; verdict remains UNVERDICTED.","rationale":"The reader correctly identified representativeness and fair configuration of the benchmark, prompts, checker, and recovery stages as the load-bearing premise, and I agree that this is where the central claim is least secure. My stress-test extends this: the supplied full text is not the paper described by the abstract, so even the internal consistency of the claimed experimental pipeline cannot be checked. The reader's UNVERDICTED verdict is appropriate because the record is insufficient for either acceptance or rejection. I mark agreement as partial rather than full because the reader's weakest_assumption focuses on generality while my primary concern is more basic: with no matching full text, there is no auditable method at all, and the abstract alone cannot support the quantitative or negative claims. If the correct manuscript were supplied and the numbers reproduced, the remaining concern would be whether the negative reasoning conclusion is falsifiable; as written, the abstract does not specify a test that could confirm or refute 'no clear evidence of underlying reasoning.' A concrete re-run with a defined checker on a held-out sample would settle the robustness of the headline metrics and would also force an explicit operationalization of plan executability and reasoning evidence.","tokens_in":12165,"tokens_out":2380,"duration_ms":31815,"concrete_test":"Obtain the true full text for arXiv:2508.01300 and verify that it matches the abstract; if it does, extract the executability checker and benchmark definitions and rerun the reported pipeline on a held-out set of 100 tasks from the same benchmark family. If the mean executable-prefix length is not within 0.5 actions of 2.65 or the success-rate gain from 21.9% to 27.5% does not replicate, the headline figures are setup-dependent rather than a robust finding about LLM planning.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that LLM-generated plans contain only a short executable prefix, that NLP-based recovery yields a small improvement, and that observable behavior shows no evidence of genuine planning reasoning. The most load-bearing condition for that claim is that the experiments behind the reported numbers (2.65 executable actions, 8.4 actions per plan, 21.9% to 27.5% success) are real, fairly configured, and sufficiently documented to audit. The supplied full text does not meet this condition: it is a different manuscript, arXiv:2508.01299v2, on Frank-Wolfe solvers for MIQCQPs, sharing no content with the abstract. Consequently, none of the benchmark definitions, LLM prompts, executability checker, or the three recovery stages is available for inspection. Even treating the abstract as the only evidence, the negative reasoning conclusion is not testable: the abstract never states what observable evidence would count as 'underlying reasoning,' so the claim could survive or fail regardless of the measured numbers. The 2.65-action average is also only meaningful if the executability checker's action semantics and plan-validation rules are defined; without them, the figure is an uninterpreted statistic. The concern is not that the reported numbers are wrong, but that the record provides no way to determine whether they characterize LLM planning ability generally or an artifact of one unshown setup.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":12337,"tokens_out":3142,"duration_ms":33186,"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":[{"comment":"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.","section":"Full text"},{"comment":"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.","section":"Abstract, findings paragraph"},{"comment":"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.","section":"Abstract"}],"minor_comments":[{"comment":"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.","section":"Abstract"},{"comment":"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.","section":"Abstract"},{"comment":"The term \"recovery pipeline\" is introduced without naming the three stages; even a brief list in the abstract would help situate the contribution.","section":"Abstract"}],"recommendation":"reject","confidential_remarks":"The full-text mismatch is a serious submission-integrity issue: the uploaded manuscript is a different arXiv paper (2508.01299) on mathematical optimization. The editor may wish to verify the submission package and consider whether this is an accidental file upload. As a referee, I cannot recommend proceeding with review on the current record, and I would not invite a revision of this record; a correct resubmission would be needed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take on arXiv:2508.01300. The record is broken: the supplied full text is not this paper. It's a math.OC manuscript on Frank-Wolfe solvers for MIQCQPs (arXiv:2508.01299). So the LLM-planning paper we're asked about exists only as its abstract. That's the first thing you should know.\n\nWhat the abstract describes is actually worth a look. Treating LLM-generated plans as NLP objects, running three repair stages, then handing off to a symbolic planner is a sensible evaluation design, and it's a fair contrast to the usual success-rate benchmarking. The reported numbers — 2.65 executable prefix actions, 8.4-action plans, success up from 21.9% to 27.5% — give concrete, comparable baselines if they're real. And the negative finding on \"no clear evidence of underlying reasoning\" is a falsifiable claim the field needs to test.\n\nBut the soft spots are big. First, the mismatch means no methods, prompts, executability checker, or benchmarks are visible. The quantitative claims are unverifiable from this record. Second, the abstract never specifies what observable evidence would count as \"underlying reasoning,\" so that conclusion is unfalsifiable as written. It could be an artifact of tasks that are too easy or too hard. Third, there are no error bars or ablations. The worry that the repair stages were tuned on the same benchmarks used for evaluation is real, but we can't even check that without the actual text.\n\nThe stress-test note is right: the problem isn't that the numbers are false, it's that the record provides no way to determine what they characterize. I agree with the reader's UNVERDICTED verdict.\n\nFor a serious editor: if the real manuscript matches this abstract and documents the pipeline, it deserves peer review. But the current record should be sent back for a corrected full text, not refereed as-is. I wouldn't cite it until I can read the actual paper.\n\nRecommendation: hold. Re-review only if the correct full text is supplied.","headline":"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.","tokens_in":12942,"tokens_out":2998,"would_cite":false,"duration_ms":37368,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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.","keywords":["large language models","AI planning","plan generation","natural language processing","plan repair","symbolic planners","executability evaluation","LLM benchmarking"],"falsifier":"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.","tokens_in":11896,"feed_emoji":"🤖","tokens_out":4164,"duration_ms":47479,"temperature":0.7,"pith_summary":"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.","feed_headline":"LLM plans average only 2.65 executable actions","feed_subtitle":"NLP-based repair lifts success from 21.9% to 27.5%, still short of classical planners.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[],"fun_headline_variants":["LLM planning shows no reasoning: 2.65 actions usable","Repair pipeline lifts LLM plan success from 21.9% to 27.5%","LLMs still fall short of symbolic planners despite repair","NLP-based plan repair only modestly boosts LLM success"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["LLM planning shows no reasoning: 2.65 actions usable","Repair pipeline lifts LLM plan success from 21.9% to 27.5%","LLMs still fall short of symbolic planners despite repair","NLP-based plan repair only modestly boosts LLM success"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000353,"raw_usage":{"total_tokens":1928,"prompt_tokens":961,"completion_tokens":967,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":577,"completion_tokens_details":{"reasoning_tokens":889}},"tokens_in":577,"tokens_out":967,"duration_ms":9661,"temperature":1.0,"reasoning_tokens":889,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T05:41:59.653096+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[],"review_version":1}