{"id":"015dfd44-8234-4514-bfb6-d87ccaf64af8","arxiv_id":"2507.05118","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"An LLM plus LTL-based verification module that reorders, inserts, and removes steps in household robot plans, reporting reduced ordering errors but with weak experimental support.","lead":"VerifyLLM uses a language model to translate household instructions into temporal logic and then checks robot action plans against that logic, fixing missing, extra, or wrongly ordered steps before execution. The paper reports better plans for a simulation benchmark, but the LTL component adds little and the evaluation lacks validation in a real simulator or robot.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The LTL component is nearly inert in the paper's own ablation, and the architecture discards temporal operators before verification; the central claim that LTL guidance significantly enhances verification quality is therefore unsupported.","rationale":"The paper's stated novelty and strongest claim is the combination of LLMs with formal LTL guidance. The algorithm's own data flow contradicts this: ExtractProps strips temporal structure before the LLM sees it, and the exemplar formula does not encode ordering. The ablation is the only direct test of LTL's contribution, and its delta is tiny relative to the gap between LLM models. This is not a claim about consensus; it is an internal consistency check between the method description, the example, and the reported numbers. A code or prompt inspection could settle it immediately. I therefore keep the reader's reject verdict: the central contribution is unsubstantiated, though the underlying LLM-only verification idea might still be useful if re-scoped. The reader's weakest assumption points to ground-truth and repair validity, which is also serious, but the more immediate load-bearing issue is the inert LTL mechanism.","tokens_in":10352,"tokens_out":5349,"duration_ms":66259,"concrete_test":"Instrument VerifyLLM to log the full LTL formula and the exact propositions passed to the LLM per window; then fix the verifying LLM and run the Zero-Shot set in two conditions: the current full-LTL prompt, and a control where the proposition list is replaced by the same number of random atomic propositions sampled from the same task vocabulary. If Order Errors and LCS do not differ significantly across 5 seeds, the LTL content is not load-bearing.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section V.A (Algorithm 1, line 11) reduces the LTL specification to ExtractProps(ϕ), so only atomic propositions are given to the LLM; the temporal operators U and G that constitute LTL's formal guidance are never used by the verifier. Section V.B's own example formula (8), F(heat water) ∧ F(add tea) ∧ F(serve), imposes no ordering, so it cannot constrain the most common failure mode (Order Errors). The central claim that combining LLMs with formal logical guidance through LTL significantly enhances verification quality is therefore structurally unsupported: the formal component is decoupled from the reasoning step. Table III is consistent with this: removing LTL changes LCS from 0.183 to 0.178 and Order Errors from 9.47 to 9.80, with no error bars or significance test. The Table II improvement is thus more plausibly due to the stronger Claude model and prompt structure than to LTL. Unless the full temporal formula is actually supplied and shown to influence decisions, the core contribution is not demonstrated.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes VerifyLLM, a pre-execution verification module for robot task plans. It translates a natural-language task description into an LTL formula via an LLM, then scans the plan with a sliding window and prompts an LLM to decide for each action whether to keep, move, remove, or augment it, with atomic propositions extracted from the LTL formula included in the prompt. The authors introduce two LTL-annotated datasets, ALFRED-LTL and VirtualHome-LTL, but the experimental section uses only the VirtualHome/Zero-Shot corpus; they report LCS similarity, missing/extra action counts, and order errors, and conclude that combining LTL with LLM verification significantly improves plan quality.","tokens_in":10556,"tokens_out":5905,"duration_ms":61964,"significance":"Reliable pre-execution verification of LLM-generated plans is an important problem, and the paper identifies a relevant error taxonomy (position errors, missing prerequisites, redundancy) and makes code available. The strongest results, however, do not isolate the contributions of the proposed components: the LTL component is reduced to atomic propositions and has a negligible effect in the ablation, the main comparison confounds model choice with method, and the metrics are computed against assumed-optimal reference plans without simulator or human validation. As it stands, the evidence does not support the paper's central claim, so the contribution is not yet established.","major_comments":[{"comment":"The verification module passes only ExtractProps(ϕ), i.e., atomic propositions, to the LLM (Algorithm 1, line 11), so the temporal operators G, U, and F from Eq. (1) never reach the verifier. The illustrative formula (8) is a conjunction of three F-clauses and imposes no ordering, yet the paper attributes the reduction in order errors to LTL 'formal logical guidance.' Section VII also concedes difficulties with complex temporal dependencies. This is a structural mismatch between the implemented method and the central claim, not a tuning issue.","section":"V.A, Algorithm 1; V.B, Eq. (8)"},{"comment":"The LTL ablation is essentially null: LCS goes from 0.183 to 0.178 and order errors from 9.47 to 9.80, with no error bars, repetitions, or significance tests; this range is easily within run-to-run noise for LLM prompts. The 'No LLM Verification' row (0.0717 LCS, 16.48 order errors) is the raw Llama-3.2-1B output from Table I, whereas 'Full System' uses Claude, so the ablation simultaneously changes the model and the method, making the claimed 74% and 60% improvements non-identifiable.","section":"VI.A.3, Table III"},{"comment":"All three baseline optimizers use Llama-3.2-1B, whereas VerifyLLM (Claude) uses a much stronger, closed-source model. Without a Claude-based baseline for CoT or Window optimization, the large gap to 0.183 LCS and 9.47 order errors cannot be attributed to the VerifyLLM architecture or to LTL; it may be entirely due to model capability. The comparison needs model-matched ablations to support the paper's claim.","section":"VI.A.2, Table II"},{"comment":"Every metric compares the corrected plan to the reference plan from VirtualHome/Zero-Shot, which is itself assumed to be the correct or optimal plan. There is no simulator execution, no human evaluation, and no check that the 'augment' or 'move' repairs are physically or temporally valid. If the LLM repairs are wrong, the pre-execution verification claim collapses; the paper provides no evidence on that point.","section":"VI, Evaluation Metrics"},{"comment":"The LTL formula is generated by the same LLM that later consumes propositions extracted from that translation, and Spot is used only for syntax validation, not semantic correctness, so there is no independent check that the formula faithfully represents the task. In addition, the window size w=5 is selected on the Zero-Shot data in Table IV and then used for the final evaluation on the same data, so the headline numbers are tuned on the test set. These issues directly affect the validity of the comparisons and are not discussed in the paper.","section":"V.B; VI.A.4"},{"comment":"The datasets ALFRED-LTL and VirtualHome-LTL are listed as contributions and the abstract promises 'rigorous testing on datasets of varying complexity,' but the experiments only use the VirtualHome Zero-Shot corpus; no ALFRED-LTL experiments, annotation protocol, or statistics are given. This leaves the claimed broad applicability and the second contribution unsubstantiated.","section":"I, VI"}],"minor_comments":[{"comment":"Equation (4) contains a typo: 'the set of transitions(E is defined as' should read 'the set of transitions E is defined as.'","section":"IV, Eq. (4)"},{"comment":"The output line of Algorithm 1 says 'Verificated sequence'; this should be 'Verified sequence.'","section":"Algorithm 1"},{"comment":"Table II does not state explicitly which LLM powers each baseline; the text should clarify that all three baselines use Llama-3.2-1B and should report decoding parameters such as temperature and the number of runs.","section":"VI.A.2, Table II"},{"comment":"Reference [11] appears mismatched: the citation for 'Can language models learn from explanations in context?' does not match the Ahn et al. author list; please check and correct the reference.","section":"References"},{"comment":"The text references 'Fig. 3' twice with different captions, and Section VI.A.5 appears to reuse the figure label for the prompt figure; renumber the figures and correct the in-text pointers.","section":"Figures"},{"comment":"Provide the exact prompts, dataset statistics, and annotation details in an appendix, since the code link alone is not sufficient for reproducibility.","section":"Appendix/Reproducibility"}],"recommendation":"reject","confidential_remarks":"For the editor: the paper's main comparison is confounded and the LTL component is inert by construction, which is a fundamental issue rather than a presentational one. The unused dataset contribution and the absence of variance or significance tests further indicate that a major revision would require substantial new experiments. I therefore recommend rejection in the current form, although a future version that actually uses temporal operators in verification and provides model-matched, significance-tested comparisons could be reconsidered."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear colleague,\n\nThe paper proposes VerifyLLM, an LLM-based pre-execution verification loop for household plans: translate the instruction to LTL, then use a sliding-window LLM to spot position errors, missing prerequisites, and redundant steps. The problem is real and the pipeline is easy to follow. The two LTL-annotated datasets are a useful resource, and releasing code is good practice.\n\nWhat actually works: the comparison against simpler optimizers (Baseline, CoT, Window) shows the VerifyLLM prompt structure helps, especially with Claude. The error-type analysis across five small models is a nice empirical summary of how bad raw LLM plans are.\n\nThe soft spots are load-bearing. First, the LTL component is nearly decorative in the implementation. Algorithm 1 passes only ExtractProps(ϕ) to the verifier; the temporal operators (U, G) are dropped. The paper's own example formula, F(heat water) ∧ F(add tea) ∧ F(serve), imposes no ordering, so it cannot constrain order errors — the most common failure mode. The claim that LTL 'significantly enhances verification quality' is not supported by the architecture. Second, the ablation confirms this: removing LTL changes LCS from 0.183 to 0.178 and order errors from 9.47 to 9.80 — likely noise, and there are no error bars or significance tests. Third, the 'No LLM Verification' row is raw Llama-3.2-1B, while the full system uses Claude; the comparison confounds model choice with method. The VerifyLLM (Llama) row in Table II is much weaker, so most of the headline gain comes from the stronger model. Fourth, the window size w=5 is selected on the Zero-Shot dataset and then used on the same dataset for the final numbers — a tuning leak. Finally, ALFRED-LTL is announced but never used, and there is no simulator or human evaluation to show the corrections are actually executable.\n\nNone of this makes the idea worthless. The authors acknowledge some limitations in the conclusion. But the central contribution — LTL-guided verification — is not demonstrated as implemented. A revision that feeds the full LTL formula (or at least ordering constraints) to the verifier, matches the base model across ablations, reports variance, and runs one ALFRED execution check would be worth refereeing. As it stands, I would not accept it, but it deserves a serious referee after major changes.\n\nBest","headline":"The LTL guidance is mostly decorative in the implementation, and the experiments don't support the headline claim; the datasets and the problem are real, but the formal component needs to actually be used.","tokens_in":11105,"tokens_out":3398,"would_cite":false,"duration_ms":35962,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A two-stage LLM-and-LTL pipeline can catch and fix errors in robot plans before they run.","keywords":["plan verification","large language models","linear temporal logic","robot task planning","pre-execution verification","household tasks","action sequence repair"],"falsifier":"Run the original and VerifyLLM-corrected plans from the VirtualHome dataset in the VirtualHome simulator and compare task success rates; if corrected plans do not succeed more often than the originals, the central pre-execution verification claim fails.","tokens_in":10168,"feed_emoji":"🤖","tokens_out":4010,"duration_ms":39510,"temperature":0.7,"pith_summary":"The paper proposes VerifyLLM, a pre-execution verification module that sits between a robot task planner and execution. Given a natural-language task description and a generated action plan, it translates the description into a Linear Temporal Logic formula and then runs a sliding-window LLM analysis over the plan, deciding for each action whether to keep, remove, move, or augment it. The goal is to catch three common failure modes: actions in the wrong order, missing prerequisites, and redundant steps. The authors claim this combination of formal logic and LLM commonsense reasoning substantially improves plan quality on household-task benchmarks, cutting ordering errors by nearly 40 percent relative to baseline optimizers when a strong LLM is used.","feed_headline":"LLM-guided verifier fixes robot plans before execution","feed_subtitle":"VerifyLLM catches wrong order, missing prerequisites, and redundant steps in household task plans.","key_machinery":"The framework couples two modules. The Translation Module converts the task description into an LTL formula using few-shot prompting, validating syntax by building a Büchi automaton with Spot and reprompting up to three times. The Verification Module slides a window of five actions over the plan; for each action it feeds the window, the task description, and atomic propositions extracted from the LTL formula to an LLM, which must output a structured JSON decision: keep, remove, move, or augment. Augment inserts a generated prerequisite; move reorders the action; remove deletes duplicates. The whole sequence is re-verified until convergence. The LTL formula is the formal scaffold; the LLM supplies the common-sense reasoning about physical preconditions and temporal order.","core_discovery":"On the paper's own terms, VerifyLLM shows that a large language model, guided by an LTL formula derived from the task instruction, can act as a general pre-execution plan verifier that repairs incorrectly positioned actions, missing prerequisites, and redundant steps. The central evidence is a comparison on VirtualHome-derived plans: with Claude as the verifier, LCS similarity to reference plans rises from 0.0717 (unverified) to 0.183, ordering errors drop from about 16.5 to 9.47, and the full system outperforms three LLM-based baselines (pairwise, chain-of-thought, and windowed) on nearly every metric. Ablations attribute most of the gain to the LLM verification step, with the LTL translation contributing a smaller but positive effect.","pith_inferences":["Since no simulator execution or human evaluation was done, the true test is whether repaired plans actually succeed more often when executed; the current metrics only measure similarity to reference plans that are themselves assumed optimal.","If the reference plans are not ground truth (e.g., contain their own errors), the reported improvements could partly reflect moving toward the references' biases rather than toward objectively correct plans.","A natural testable extension is to feed VerifyLLM-corrected VirtualHome plans into the VirtualHome simulator and compare task success rates against both the original LLM plans and the reference plans.","The small ablation contribution of LTL suggests that the formal component may be replaceable by a plain structured prompt; a direct comparison could isolate whether temporal logic adds value beyond annotated examples."],"forward_implications":["Adding such a verification module to an LLM-based planner lets the planner rely on commonsense constraints without hand-coding every precondition into the domain model.","The three error types specifically targeted — missing prerequisites, redundant steps, and ordering mistakes — are directly tied to execution failures and wasted steps, so correcting them before execution should improve downstream success rates.","The sliding-window size of 5 is a tuned parameter: a window too small loses context and one too large introduces noise, making context selection a key design choice for the approach.","Verification quality scales with the LLM used; larger models and stronger reasoning models yield substantially better plan repairs, suggesting model choice is a dominant factor in performance.","Ablations show the LTL translation matters less than the LLM reasoning, implying the formal layer's main role is guiding prompts rather than enforcing correctness on its own."],"supporting_citations":[{"why":"Supplies the Zero-Shot planner and the generated plans used as input to VerifyLLM.","marker":"[6]"},{"why":"Provides the VirtualHome dataset with household task instructions and reference action sequences used for evaluation.","marker":"[18]"},{"why":"Prior common-sense plan verification with LLMs that VerifyLLM extends.","marker":"[12]"},{"why":"Source for the derived ALFRED-LTL dataset.","marker":"[17]"},{"why":"Foundational LTL formalism the translation module relies on.","marker":"[27]"},{"why":"LTL for robotic mission and motion planning, motivating the formal framework.","marker":"[13]"}],"fun_headline_variants":["LLM verifier catches robot plan flaws pre-run","Pre-execution robot plan check with LLM","VerifyLLM uses LLM to fix robot plans upfront","LLM spots errors in robot task plans early","Verify robot plans with LLM before execution"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole evaluation assumes the LLM's repair decisions are correct and that the reference plans are the true optimal plans; if the repairs are wrong or the references are not ground truth, the claimed verification quality is not established.","fun_headline_variants_meta":{"raw":{"variants":["LLM verifier catches robot plan flaws pre-run","Pre-execution robot plan check with LLM","VerifyLLM uses LLM to fix robot plans upfront","LLM spots errors in robot task plans early","Verify robot plans with LLM before execution"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000147,"raw_usage":{"total_tokens":1143,"prompt_tokens":860,"completion_tokens":283,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":476,"completion_tokens_details":{"reasoning_tokens":210}},"tokens_in":476,"tokens_out":283,"duration_ms":3663,"temperature":1.0,"reasoning_tokens":210,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T19:32:14.424730+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the original and VerifyLLM-corrected plans from the VirtualHome dataset in the VirtualHome simulator and compare task success rates; if corrected plans do not succeed more often than the originals, the central pre-execution verification claim fails.","supporting_citations":[{"cited_title":"Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,","cited_arxiv_id":null,"evidence_quote":"Supplies the Zero-Shot planner and the generated plans used as input to VerifyLLM."},{"cited_title":"Virtualhome: Simulating household activities via programs,","cited_arxiv_id":null,"evidence_quote":"Provides the VirtualHome dataset with household task instructions and reference action sequences used for evaluation."},{"cited_title":"Common sense plan verification with large language models,","cited_arxiv_id":null,"evidence_quote":"Prior common-sense plan verification with LLMs that VerifyLLM extends."},{"cited_title":"The temporal logic of programs,","cited_arxiv_id":null,"evidence_quote":"Foundational LTL formalism the translation module relies on."}],"review_version":1}