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REVIEW 2 major objections 4 minor 36 references

Zero-shot LLM planners inside a formal task-and-motion-planning loop are less successful and slower than the engineered planners they replace; geometric detail in prompts makes task errors worse.

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LLM-based planners inside an LLM-Modulo TAMP loop have lower success rates and higher planning times than engineered planners, and fast direct variants outperform slow thinking variants in most settings.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection Useful systematic negative result on LLM-Modulo TAMP, but the geometric-details headline is overclaimed due to a confounded comparison. the 2 major comments →

arxiv 2510.00182 v2 pith:AJZHA3SN submitted 2025-09-30 cs.RO cs.AI

A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream

Jorge Mendez-Mendez This is my paper
classification cs.RO cs.AI
keywords large language modelstask and motion planningPDDLStreamLLM-Modulozero-shot planninggeometric reasoningplanner evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 whether an LLM-Modulo framework—where a large language model proposes plans and an engineered verifier checks and corrects them—is a viable strategy for task and motion planning (TAMP). To answer, it constructs 16 planners by replacing parts of two existing PDDLStream-based TAMP solvers with calls to a single LLM, varying what the LLM produces (action plans, geometric poses, both separately, or both in one integrated response) and whether the LLM is allowed to 'think' before answering. Across 4,950 zero-shot problems in three domains, every LLM variant had lower success rates and higher planning times than the engineered baselines. The paper also reports that providing geometric scene details increased the number of task-level (PDDL) errors, and that fast direct generation beat slower reasoning variants in most comparisons. A sympathetic reader would take away that, for now, the efficient division of labor is quick LLM proposal followed by formal TAMP verification.

Core claim

The paper's central claim is that LLM-based planners embedded in an LLM-Modulo TAMP loop do not match engineered TAMP systems, and that design choices matter less than the presence of the verifier. In its zero-shot experiments, the LLM solved many problems but succeeded less often and far more slowly than the base solvers; most LLM failures were timeouts, with a substantial minority being the LLM 'giving up' on solvable problems. The authors further claim that augmenting prompts with geometric information increases PDDL-level errors for non-thinking variants, contrary to the intuition that geometry-aware prompting helps integrated reasoning, and that DIRECT (zero thinking budget) variants ou

What carries the argument

The carrying mechanism is the LLM-Modulo loop built on PDDLStream: the LLM generates candidate symbolic plans, sampled poses, or both; the base TAMP algorithm (ADAPTIVE or BILEVEL) verifies candidate outputs against stream certificates and PDDL preconditions, reprompting the LLM on failure. The 16 algorithms arise from crossing base algorithm (ADAPTIVE/BILEVEL), the component the LLM substitutes (PDDL planning, pose-stream evaluation, both separately, or integrated generation), and thinking budget (DIRECT vs THINKING). PDDLStream's 'streams'—procedures that certify geometric facts like stable placement or collision-free motion—are what let the verifier do formal geometric checking that the L

Load-bearing premise

The claim that geometric details increase PDDL errors assumes the integrated variant differs from the PDDL-only variant only by the added geometric information, but integrated variants also require the LLM to produce actions and samples in a single joint response, so the extra errors could come from the joint-generation burden instead.

What would settle it

Rerun the INTEGRATED planner with the geometric object descriptions stripped from the prompt while keeping the joint-generation requirement; if the PDDL error rate drops to the level of PDDL-only variants, the added errors are caused by geometric detail, and if it stays high, the cause is joint generation. Either outcome settles the paper's causal claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Engineered TAMP solvers remain more reliable and faster than zero-shot LLM-based planners on these problems.
  • In an LLM-Modulo loop, the efficient configuration is fast direct LLM generation followed by formal verification; spending LLM computation on internal reasoning is usually not repaid.
  • Adding geometric detail to prompts does not help the LLM plan better and can degrade its symbolic planning, at least for non-thinking variants.
  • LLMs can solve some novel TAMP problems zero-shot when embedded in a verifier loop, but resource limits (timeouts, token caps) account for most failures.
  • Failure analysis shows LLMs sometimes assert that solvable problems are unsolvable, a failure mode that extra computation will not fix.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A testable extension: rerun the integrated variants with geometric descriptions removed from the prompt but with the same joint-generation requirement; if PDDL errors remain high, the true driver is dual-task generation, not geometric distraction.
  • The finding that verification beats internal thinking suggests a general design principle for LLM-Modulo systems beyond TAMP: keep proposal generation cheap and put all expensive reasoning in the formal checker.
  • Because the study uses a single LLM and three domains, the headline result may understate or overstate what future models can do; evaluating the same 16 architectures on a stronger model would clarify whether the bottleneck is this LLM or the modular design.
  • The 'giving up' failures suggest an easy intervention: instruct the LLM to output partial plans or pose candidates even when it doubts global solvability, letting the verifier judge.
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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 / 4 minor

Summary. This paper reports a systematic empirical comparison of 16 LLM-Modulo planners for PDDLStream task and motion planning against two engineered TAMP baselines, ADAPTIVE and BILEVEL, across seven instantiations of three benchmark domains. The LLM variants use Gemini 2.5 Flash in zero-shot mode to generate PDDL plans, pose samples, or both, and the TAMP verifier reprompts on failure. Each condition is run 50 times, and significance is assessed with paired exact McNemar tests and Holm-corrected compact letter displays. The central finding is that the LLM-based planners generally have lower success rates and higher planning times than the engineered baselines; direct (non-thinking) variants outperform thinking variants in most comparisons; and INTEGRATED variants, which jointly generate plans and poses, perform worst. The paper interprets these results as evidence that geometric details in the prompt increase task-planning errors and that formal TAMP verification should carry the geometric reasoning burden.

Significance. If the result holds, this is a useful systematic negative result for the LLM-Modulo approach to TAMP. The study is not a single anecdote: it uses a large paired design, exact statistical tests, multiple domains, and public code, prompts, and dialogs. The main success-rate and timing comparisons are well supported and will be of interest to the robotics planning community. The paper is not circular: the baselines are engineered PDDLStream systems from prior work, and no fitted parameters or author-specific assumptions enter the evaluation. However, one of the three headline findings—that geometric details cause more task-planning errors—is not identified by the experimental design, and the abstract contains an inconsistent evaluation count. These issues must be addressed before the paper is published.

major comments (2)
  1. [Section V.B.3 and Section VII, Fig. 7a] The causal claim that 'providing geometric details increases the number of task-planning errors' is not isolated by the comparison. INTEGRATED differs from PDDL-only and PDDL+POSES on two axes: it adds geometric object descriptions to the prompt, and it requires the LLM to produce a PDDL plan and pose samples in a single joint generation. The extra PDDL errors in Fig. 7a could therefore come from the joint-generation burden, longer prompts, or output-format demands rather than from geometric information per se. The sentence in Section VII stating that these methods 'only differ in the PDDL prompt in the inclusion of geometric information' is inaccurate. Since this is one of the three headline findings, please add an ablation (e.g., INTEGRATED without geometric descriptions, or PDDL generation with geometric descriptions appended) or rephrase the claim as an association rather than a caus
  2. [Abstract vs. Sections I and VI] The abstract reports '13750 evaluations,' but the full text reports 4,950 problems (Section I) and a protocol of 50 trials per algorithm-domain combination (Section VI-B). The described design yields 4,950 runs: 18 conditions in Blocked and the three Packing domains and 9 conditions in the three Rovers domains, each with 50 trials. Please state one consistent count and define what counts as an evaluation. A mismatch of this magnitude in the headline quantitative claim undermines reader confidence in the reported scope.
minor comments (4)
  1. [Section V.B.1] The sentence 'our early tests suggested that including geometric details instead harms performance' is presented without supporting data. Since this motivated the design choice for all PDDL prompts, please include the early-test results in an appendix or clearly label this as a preliminary observation.
  2. [Section VI-C, Fig. 6] The Wilcoxon timing comparisons are performed only on successful runs and only for algorithms with success rate at least 30%. Please report the exact number of pairwise comparisons included, and note the selection bias that arises when comparing only on problems where both algorithms succeed.
  3. [Section VI-B, footnote 2] The footnote states that the Gemini API does not guarantee deterministic outputs even when the seed is set. Please also report the model version/date, sampling temperature, and any other decoding parameters used, so that the experiments are as reproducible as the API permits.
  4. [Section VII] The text says 'all LLM-based methods solve a small portion of the problems in Rovers domains' and later 'success rates below 15%.' Please add the exact baseline success rate for ADAPTIVE in Rovers for comparison, since the CLD in Fig. 4(e)–(g) already shows it as the only high-performing method.

Circularity Check

0 steps flagged

No significant circularity: the paper is an empirical benchmark against external PDDLStream domains and engineered TAMP baselines.

full rationale

The paper's central claims are empirical measurements, not derivations. The 16 LLM-based planners are constructed by substituting TAMP components with LLM calls, and their success rates, planning times, and failure modes are compared against ADAPTIVE and BILEVEL baselines from prior external literature (Garrett et al. 2020; Kumar et al. 2023). No parameter is fitted to the data and then renamed as a prediction; no result is defined in terms of another result it is supposed to establish; and no load-bearing argument rests on a self-citation, since the author has no prior cited work. The geometric-details claim ('providing geometric details increases the number of task-planning errors') is a comparative empirical finding, though the comparison between INTEGRATED and PDDL variants is confounded because INTEGRATED differs in both prompt geometry and joint PDDL+pose generation. That is a validity concern about causal attribution, not a circularity: the INTEGRATED condition is not defined in terms of the PDDL-only condition, and the observed error counts are independent measurements. Likewise, the abstract/full-text discrepancy in the number of evaluations (13,750 vs. 4,950) is a reporting inconsistency, not a circular step. The benchmark is self-contained and externally grounded, so the appropriate finding is no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 0 invented entities

The paper introduces no new theoretical entities or fitted parameters. Its conclusions rest on empirical comparisons, the correctness of the base TAMP verifiers, the representativeness of one LLM, and the assumption that zero-shot prompts prevent data leakage.

axioms (4)
  • domain assumption ADAPTIVE and BILEVEL are correct and probabilistically complete TAMP verifiers for the tested domains.
    The comparison treats the engineered TAMP systems as ground truth. If they accept invalid plans or reject valid ones, the success-rate gap could be distorted. The paper inherits these guarantees from Garrett et al. [1] and Kumar et al. [3].
  • domain assumption Gemini 2.5 Flash is representative enough of current LLMs for the paper's 'LLM-based planners' conclusions.
    All experiments use one model. The paper acknowledges this in the limitations but the generalizable claims are framed broadly.
  • domain assumption Zero-shot prompting prevents the specific evaluation instances from having been seen during pretraining.
    The authors state 'we have no guarantees that the LLM did not encounter similar problems in its training data.' The 'zero-shot' framing is thus an assumption about generalization, not a verified fact.
  • domain assumption The three PDDLStream domains (Blocked, Packing, Rovers) from Garrett et al. are representative of TAMP problems.
    Conclusions about TAMP viability are drawn from seven difficulty levels built from a single prior benchmark suite.

reviewed 2026-08-04 · how reviews work

0 comments
Cite this review

Pith. "Pith review of A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream." pith.science (2026). https://pith.science/paper/AJZHA3SN

@misc{pith2026251000182,
  author       = {Pith},
  title        = {Pith review of: A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJZHA3SN}},
  note         = {Machine review of arXiv:2510.00182}
}
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read the original abstract

While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics. One promising direction is to integrate the semantic knowledge of LLMs with the formal reasoning of task and motion planning (TAMP). However, designing such systems is complicated by the myriad of choices for how to integrate LLMs within TAMP. We develop 16 algorithms that use LLMs to substitute key TAMP components. Our zero-shot experiments across 13750 evaluations and three domains reveal that LLM-based planners exhibit lower success rates and higher planning times than engineered systems. Providing geometric details increases the number of task-planning errors compared to pure PDDL descriptions, and (faster) direct LLM variants outperform (slower) reasoning variants in most cases. Code and results are available at https://github.com/jorge-a-mendez/llm-pddlstream.

Figures

Figures reproduced from arXiv: 2510.00182 by Jorge Mendez-Mendez.

Figure 1
Figure 1. Figure 1: Rovers domain [1]. Two Turtlebots must acquire one rock sample (black patch) and one soil sample (brown patch), photograph k = 4 objectives (blue boxes), and send results to the Husky. Obstacles limit visibility and traversability. LLMs possess this generalization capability, we thoroughly evaluate 16 LLM-based planners on TAMP problems. Our main focus is on understanding the trade-offs imposed by various … view at source ↗
Figure 2
Figure 2. Figure 2: Summary of the 16 LLM-based planners. With classical PDDL planners (e.g., Fast Downward), ADAPTIVE is often faster. One reason is that PDDL planning is much faster than stream evaluation, making it cheap to generate many candidate plans. Another reason is that PDDL planners are not aware of any aspects of the scene geometry that are not expressed in PDDL. Therefore, they cannot assess whether an object may… view at source ↗
Figure 3
Figure 3. Figure 3: Left: Blocks domain [1]. PR2 must place one of the blue boxes on the green region, but the nearest box is blocked. Right: Packing domain with k = 5 objects [1]. PR2 must place all boxes on the green region, avoiding collisions. c) Subsequent prompts: We provide details about any previous plan failures, including PDDL (e.g., missing precon￾ditions) and stream (e.g., no collision-free grasps) failures. 2) PO… view at source ↗
Figure 4
Figure 4. Figure 4: Success rate across 50 problems per algorithm and domain (compact letter display atop bars). When considering all [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Fraction of 50 problems per domain that failed for each reason per algorithm. Most failures occur due to timeouts, [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average time for successful runs per algorithm [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Heatmaps of metrics related to algorithm operation, averaged across 50 problems per domain. (d) does not include [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Dollar amount per problem spent on Gemini requests for each domain and algorithm. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

discussion (0)

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This paper was first reviewed by deepseek-v4-flash on August 4, 2026.