LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models

Daegyu Lee; Seongmin Kim

REVIEW 3 major objections 2 minor 22 references

LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models

T0 review · 3 major / 2 minor · reviewed 2026-05-07 · grok-4.3

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Pith's one-line read A locally hosted LLM given only a PDDL domain file can generate relaxation rules, manage failure recovery, and perform zero-shot object scoring to automate neuro-symbolic robotic planning and raise average success rate from 0.828 to 0.945.

desk verdict LLM-Flax automates rule generation and zero-shot scoring for neuro-symbolic planners with reported success rate gains, but lacks independent checks that the LLM outputs preserve domain semantics. read the letter →

arxiv 2604.26569 v1 submitted 2026-04-29 cs.RO

Seongmin Kim , Daegyu Lee This is my paper · ORCID

classification cs.RO

keywords neuro-symbolicplanninglargelanguagemodelsPDDLdomainsrobotictaskrelaxationruleszero-shotscoringfailurerecoverymazenavigation

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

The paper presents LLM-Flax as a three-stage system that removes the need for domain experts to write rules or collect training data for graph neural networks in neuro-symbolic task planning. Stage 1 uses structured prompting on the PDDL file to create relaxation and complementary rules with built-in validation and self-correction. Stage 2 adds a feasibility-gated budget policy for LLM-guided recovery that accounts for latency before each call. Stage 3 substitutes a trained GNN with direct LLM-based object importance scoring. Tested on eight MazeNamo grid benchmarks, the full system matches or exceeds manual performance on every case and succeeds on instances where the hand-crafted baseline scores zero. This matters because it makes it feasible to apply advanced planners to new robotic domains without repeated expert intervention or data collection.

What carries the argument

The three-stage LLM-Flax framework that automates rule generation, feasibility-gated failure recovery, and zero-shot LLM object importance scoring from a single PDDL domain file.

What would settle it

Running LLM-Flax on a fresh PDDL domain and finding that the generated rules produce invalid plans or that the zero-shot scores yield success rates well below a carefully tuned manual baseline would falsify the claim of reliable full automation.

Watch

Extended reading notes

Core claim

LLM-Flax is a three-stage framework that, given only a PDDL domain file, lets a locally hosted LLM (1) generate relaxation and complementary rules through structured prompting with format validation and self-correction, (2) handle failure recovery via a feasibility-gated budget policy that reserves latency cost before each call, and (3) replace a trained GNN with zero-shot object importance scoring. On the MazeNamo benchmark across 10x10, 12x12, and 15x15 grids, the complete system reaches an average success rate of 0.945 compared with the manual baseline's 0.828, matching or beating the manual planner on all eight cases; it records 0.733 success on 12x12 Expert where the manual planner gets

Load-bearing premise

A locally hosted LLM can consistently produce correct and complete relaxation rules plus accurate zero-shot object importance scores without introducing errors that break the soundness or completeness of the downstream planner.

Editorial extensions

If this is right

New robotic domains can be planned without requiring experts to author relaxation or complementary rules.
No training problems or supervised GNN data are needed to obtain competitive object scoring.
Planning remains sound on hard instances such as 12x12 Expert grids where manual rules fail completely.
Latency cost can be explicitly budgeted before each LLM call to avoid starving the relaxation fallback.

Reading between the lines

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

The same prompting pipeline could be applied to other PDDL domains such as robotic manipulation or logistics without maze-specific tuning.
Larger context windows or better long-context LLMs would directly address the noted bottleneck that limits Stage 3 on bigger instances.
Combining the automated rules with existing symbolic planners could create hybrid systems that inherit both generality and formal guarantees.
Local hosting preserves privacy but requires that the chosen LLM be capable enough to avoid systematic rule errors that would otherwise need human debugging.

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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.

Desk Editor's Note

read the letter

The main thing to know is that LLM-Flax uses a local LLM to automatically create relaxation rules, handle planning failures, and score objects for neuro-symbolic robotic task planning, leading to better success rates than manual methods on the MazeNamo benchmarks. The approach cuts down on expert effort and training needs. What is new is the combination of structured prompting with validation for rule generation, a feasibility-gated policy for LLM-based recovery, and zero-shot LLM scoring that replaces the GNN entirely. The paper evaluates this on eight benchmarks covering different grid sizes and difficulties. It achieves an average success rate of 0.945 versus 0.828 for the manual baseline, and it succeeds in cases like the 12x12 Expert where the baseline gets zero success. The soft spots are around verification of the LLM outputs. While there is format validation and self-correction, the paper does not provide independent tests to ensure the rules match the original PDDL semantics exactly or that the importance scores do not introduce bias. This leaves open the possibility that performance relies on the LLM's pre-trained knowledge rather than the domain file alone. The context window issue for scaling is also a practical limit. This paper is for robotics and AI researchers interested in making neuro-symbolic planners easier to deploy in new domains. It will be valuable to those exploring LLM uses in planning systems. The work engages honestly with the challenges of these methods and presents concrete results. It deserves a serious referee to dig into the experimental details and the prompting strategies. I would recommend sending this to peer review.

Referee Report

3 major / 2 minor

Summary. The paper introduces LLM-Flax, a three-stage neuro-symbolic framework that uses a locally hosted LLM, given only a PDDL domain file, to automate generation of relaxation and complementary rules (Stage 1 with format validation and self-correction), LLM-guided failure recovery via a feasibility-gated budget policy (Stage 2), and zero-shot object importance scoring to replace a trained GNN (Stage 3). Evaluated on eight MazeNamo benchmarks (10x10, 12x12, 15x15 grids), it reports an average success rate of 0.945 versus 0.828 for a manual baseline (+0.117), with specific gains such as 0.733 vs. 0.000 on 12x12 Expert and 1.000 vs. 0.900 on 15x15 Hard.

Significance. If the soundness of the LLM-generated components and the reported gains hold under verification, the work would meaningfully reduce manual effort in deploying neuro-symbolic planners for robotic tasks, enabling faster adaptation to new domains without expert rule authoring or supervised GNN training. The zero-shot scoring and latency-aware recovery are notable technical contributions, though the acknowledged context-window limits highlight a key scalability issue for larger problems.

major comments (3)

[Abstract and evaluation results] Abstract and evaluation results: The headline success-rate claims (average 0.945 vs. 0.828, plus per-benchmark numbers such as 0.733 vs. 0.000 on 12x12 Expert) are load-bearing for the central thesis, yet no details are provided on the number of evaluation runs per benchmark, statistical significance tests, error bars, or variance; without these, it is impossible to determine whether the gains are robust or could be explained by selection effects or run-to-run variability in LLM outputs.
[Stage 1] Stage 1 (rule generation): The framework relies on the LLM producing relaxation and complementary rules whose semantics exactly match the input PDDL domain (no added or removed transitions), but the manuscript describes only format validation and self-correction without any independent soundness check such as equivalence testing, model checking on small instances, or exhaustive enumeration; if the generated rules are incomplete or inconsistent, the downstream planner could succeed on the reported test set while violating the original domain semantics.
[Stage 3] Stage 3 (zero-shot scoring): Replacing the trained GNN with zero-shot LLM object importance scoring is a core innovation, yet the paper provides no analysis or ablation of ranking errors (e.g., critical objects omitted or irrelevant ones over-ranked) and only notes a context-window bottleneck without quantifying its impact via scaling experiments on larger grids or domains; this directly affects the claim of full elimination of training data.

minor comments (2)

[Stage 2] The description of the feasibility-gated budget policy in Stage 2 would benefit from a concrete pseudocode listing or parameter values (e.g., exact budget thresholds) to enable reproduction.
[Evaluation] A table summarizing per-benchmark success rates, including run counts and any ablation results for the three stages, would improve clarity over the narrative presentation in the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful review and for highlighting areas where additional rigor would strengthen the presentation of LLM-Flax. We address each major comment below and commit to revisions that improve clarity and verifiability without altering the core claims.

read point-by-point responses

Referee: [Abstract and evaluation results] The headline success-rate claims (average 0.945 vs. 0.828, plus per-benchmark numbers such as 0.733 vs. 0.000 on 12x12 Expert) are load-bearing for the central thesis, yet no details are provided on the number of evaluation runs per benchmark, statistical significance tests, error bars, or variance; without these, it is impossible to determine whether the gains are robust or could be explained by selection effects or run-to-run variability in LLM outputs.

Authors: We agree that the absence of run counts, variance measures, and statistical tests limits the ability to assess robustness. The results in the current manuscript reflect single executions per benchmark. In the revision we will re-run every benchmark across 10 independent trials (varying LLM sampling seeds and environment initializations), report means with standard deviations, add error bars to the results table, and include paired statistical tests (e.g., t-tests) against the manual baseline. These additions will appear in both the abstract and the evaluation section. revision: yes
Referee: [Stage 1] The framework relies on the LLM producing relaxation and complementary rules whose semantics exactly match the input PDDL domain (no added or removed transitions), but the manuscript describes only format validation and self-correction without any independent soundness check such as equivalence testing, model checking on small instances, or exhaustive enumeration; if the generated rules are incomplete or inconsistent, the downstream planner could succeed on the reported test set while violating the original domain semantics.

Authors: The observation is correct: our validation is currently limited to syntactic format checks and iterative self-correction. We will add an independent soundness verification step in the revised manuscript. Specifically, we will apply a PDDL model checker to small, exhaustively enumerable instances derived from each domain to confirm that the generated relaxation and complementary rules preserve the original transition semantics. Results of these checks will be reported; any detected discrepancies will be discussed and the prompting procedure adjusted if needed. revision: yes
Referee: [Stage 3] Replacing the trained GNN with zero-shot LLM object importance scoring is a core innovation, yet the paper provides no analysis or ablation of ranking errors (e.g., critical objects omitted or irrelevant ones over-ranked) and only notes a context-window bottleneck without quantifying its impact via scaling experiments on larger grids or domains; this directly affects the claim of full elimination of training data.

Authors: We accept that a quantitative characterization of ranking quality and context-window effects is missing. In the revision we will insert an ablation that compares LLM-generated importance rankings against oracle rankings obtained from solved plans, reporting precision/recall for critical objects and the frequency of over- or under-ranking. We will also add scaling experiments on 20x20 grids (and larger where feasible) that measure success-rate degradation and latency as context limits are approached. These results will qualify the scope of the 'no training data' claim for the evaluated problem sizes. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical evaluation on external benchmarks

full rationale

The paper introduces a three-stage LLM-based framework for generating relaxation rules, failure recovery, and zero-shot object scoring from a PDDL domain file alone. All central claims are supported by direct success-rate measurements on the external MazeNamo benchmark suite (10x10/12x12/15x15 grids) against an independently authored manual baseline. No equations, fitted parameters, or derivations are defined in terms of the target performance metrics; the reported SR gains (0.945 avg vs 0.828) are raw empirical outcomes, not quantities forced by construction or self-citation chains. The framework description contains no self-referential definitions, ansatz smuggling, or uniqueness theorems imported from prior author work.

Assumptions & free parameters 0 free parameters · 2 assumptions · 1 invented entities

The framework rests on assumptions about LLM reliability for structured rule generation and zero-shot scoring that are demonstrated only at the level of the abstract; no free parameters or new physical entities are introduced.

assumptions (2)

domain assumption Structured prompting with format validation and self-correction can produce valid relaxation and complementary rules from a PDDL domain file alone.
This is the core of Stage 1.
domain assumption Zero-shot LLM object importance scoring can substitute for a domain-trained GNN without degrading planner performance.
This is the core of Stage 3.

invented entities (1)

LLM-guided failure recovery with feasibility-gated budget policy
purpose: To handle planner failures while reserving API latency budget and preventing fallback starvation.
New policy introduced in Stage 2.

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Cite this review

Pith. "Pith review of LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models." pith.science (2026). https://pith.science/paper/2604.26569

@misc{pith2026260426569,
  author       = {Pith},
  title        = {Pith review of: LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.26569}},
  note         = {Machine review of arXiv:2604.26569}
}

read the original abstract

Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementary rules, and hundreds of training problems must be solved to supervise a Graph Neural Network (GNN) object scorer. We propose LLM-Flax, a three-stage framework that eliminates all three sources of manual effort using a locally hosted LLM given only a PDDL domain file. Stage 1 automatically generates relaxation and complementary rules via structured prompting with format validation and self-correction. Stage 2 introduces LLM-guided failure recovery with a feasibility-gated budget policy that explicitly reserves API latency cost before each LLM call, preventing the downstream relaxation fallback from being starved. Stage 3 replaces the domain-trained GNN entirely with zero-shot LLM object importance scoring, requiring no training data. We evaluate all three stages on the MazeNamo benchmark across 10x10, 12x12, and 15x15 grids (8 benchmarks total). LLM-Flax achieves average SR 0.945 versus the manual baseline's 0.828 (+0.117), matching or outperforming manual rules on every one of the eight benchmarks. On 12x12 Expert, LLM-Flax attains SR 0.733 where the manual planner fails entirely (SR 0.000); on 15x15 Hard, it achieves SR 1.000 versus Manual's 0.900. Stage 3 demonstrates feasibility (SR 0.720 on 12x12 Hard with no training data) but faces a context-window bottleneck at scale, pointing to the primary open challenge for future work.

Figures

Figures reproduced from arXiv: 2604.26569 by the authors.

**Figure 1.** Overview of LLM-Flax. Given only a PDDL domain file, Stage 1 automatically generates relaxation and complementary view at source ↗

**Figure 2.** Detailed architecture of LLM-Flax. Left: Stage 1 generates relaxation and complementary rules offline from the PDDL domain file via structured LLM prompting with validation and self-correction. Center: At test time, the three-step Flax planning loop (Step 1: threshold-decay pruning; Step 2: relaxation; Step 3: complementary expansion) is shared by all stages. Right: Stage 2 inserts a feasibility-gated LLM recovery c… view at source ↗

**Figure 3.** Success rate vs. difficulty (Flax vs. LLM-Flax) across all grid sizes. Shaded columns mark benchmarks where LLM-Flax view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: LLM-Flax pipeline on a 10×10 hard MazeNamo problem (problem 16, 163 objects). (a) Full problem with all objects. (b) Zero-shot LLM relevance scores assigned to each object (Stage 3): goal scores 1.00, nearby obstacles score 0.5–0.9, distant objects score low. (c) After…

**Figure 5.** Figure 5: Compact SR heatmap for all four configurations across all eight benchmarks. All LLM models are utilized only for

**Figure 6.** Figure 6: Qualitative planning traces for all eight benchmarks. Each row is one benchmark (label at left); columns show

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed May 7, 2026 · model on record in the stance chip above.

[1] [1]

The fast downward planning system,

M. Helmert, “The fast downward planning system,” vol. 26, 2006, pp. 191–246

work page 2006

[2] [2]

PDDL2.2: The language for the classical part of the 4th international planning competition,

S. Edelkamp and J. Hoffmann, “PDDL2.2: The language for the classical part of the 4th international planning competition,” 2004

work page 2004

[3] [3]

Planning with learned object importance in large problem instances,

T. Silver, K. Allen, A. Lew, L. P. Kaelbling, and J. Tenenbaum, “Planning with learned object importance in large problem instances,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 13, 2021, pp. 11 738–11 746

work page 2021

[4] [4]

Graph-ploi: Graph neural networks for planning with learned object importance,

Y . Chenet al., “Graph-ploi: Graph neural networks for planning with learned object importance,” inInternational Conference on Robotics and Automation, 2024

work page 2024

[5] [5]

Fast task planning with neuro-symbolic relaxation,

Q. Du, B. Li, Y . Du, S. Su, T. Fu, Z. Zhan, Z. Zhao, and C. Wang, “Fast task planning with neuro-symbolic relaxation,”IEEE Robotics and Automation Letters, 2026, arXiv:2507.15975

work page arXiv 2026

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Language models are few-shot learners,

T. Brown, B. Mann, N. Ryder,et al., “Language models are few-shot learners,”Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020

work page 1901

[7] [7]

Chain-of-thought prompting elicits reasoning in large language models,

J. Wei, X. Wang, D. Schuurmans,et al., “Chain-of-thought prompting elicits reasoning in large language models,”Advances in Neural Infor- mation Processing Systems, vol. 35, pp. 24 824–24 837, 2022

work page 2022

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J. Hoffmann and B. Nebel, “The FF planning system: Fast plan genera- tion through heuristic search,”Journal of Artificial Intelligence Research, vol. 14, pp. 253–302, 2001

work page 2001