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arxiv: 2510.05486 · v2 · submitted 2025-10-07 · 💻 cs.CL

Language Model as Planner and Formalizer under Constraints

Cassie Huang , Stuti Mohan , Ziyi Yang , Stefanie Tellex , Li Zhang This is my paper

Pith reviewed 2026-05-18 09:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords large language modelsplanningconstraintsrobustnessformal languagesbenchmarksreasoning
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The pith

Adding one-sentence constraints halves LLM performance in planning tasks

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how well large language models manage planning when extra natural language constraints are present. It augments standard benchmarks with manually annotated constraints drawn from four defined categories. Experiments cover four state-of-the-art reasoning models, four formal languages, and four datasets, with models tested both as direct planners and as translators into formal representations. Results show that these added constraints reduce performance by roughly half. A sympathetic reader would care because this indicates current models may fail on realistic tasks that include specific rules, with possible safety implications for applications.

Core claim

By augmenting widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories, the introduction of one-sentence constraints consistently halves performance over 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets, indicating current LLMs' lack of robustness.

What carries the argument

Augmentation of standard planning benchmarks with one-sentence constraints from four categories, applied to test LLMs in planner and formalizer roles.

If this is right

  • Standard planning benchmarks without constraints may overestimate LLM abilities.
  • Downstream planning tasks using LLMs risk safety issues when real constraints appear.
  • Future evaluations of planning should routinely include rich constraint sets.
  • Work on improving LLM robustness to constraints becomes a clear research direction.

Where Pith is reading between the lines

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

  • Models may need explicit training on constraint integration to close the observed gap.
  • Similar robustness tests could apply to other LLM tasks that involve rule following.
  • Conflicting constraints or larger numbers of them might expose even greater drops.

Load-bearing premise

The manually annotated constraints accurately represent fine-grained real-world constraints without introducing unintended biases or extra complexity beyond the four intended categories.

What would settle it

Re-running the same experiments on the augmented benchmarks and observing no substantial performance drop when the one-sentence constraints are included would falsify the claim.

read the original abstract

LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning domain and problem in a formal language that can derive plans deterministically. However, both lines of work rely on standard benchmarks that include only generic and simplistic environmental specifications, leading to potential overestimation of the planning ability of LLMs and safety concerns in downstream tasks. We bridge this gap by augmenting widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories. Over 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets, we show that the introduction of one-sentence constraints consistently halves performance, indicating current LLMs' lack of robustness and an avenue for future research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper augments standard planning benchmarks with manually annotated one-sentence natural language constraints spanning four formally defined categories. It evaluates four state-of-the-art reasoning LLMs acting as planners or formalizers across four formal languages and four datasets, reporting that the added constraints consistently halve performance and thereby demonstrate current LLMs' lack of robustness to fine-grained constraints.

Significance. If the central empirical result holds after validation of the annotations, the work is significant because it supplies a concrete, multi-model/multi-language/multi-dataset measurement of LLM fragility in constrained planning—an issue directly relevant to safety-critical applications. The construction of a new constrained benchmark and the breadth of the experimental sweep are clear strengths that would support follow-on research on robustness improvements.

major comments (2)
  1. [§3] §3 (Annotation and Constraint Categories): The manuscript provides no quantitative validation of the manual annotations (e.g., inter-annotator agreement, distribution of constraint length or syntactic complexity versus the original instances, or ablation removing individual categories). Without these controls it remains possible that the observed performance halving partly reflects harder or noisier test instances rather than fragility to the intended constraint semantics alone; this directly affects the load-bearing claim that the drop measures robustness.
  2. [§4] §4 (Experimental Results): The paper states that performance is “consistently halved” but reports neither per-instance metrics, statistical significance tests, nor confidence intervals on the deltas. This omission prevents readers from assessing whether the halving is uniform or driven by a subset of instances or categories.
minor comments (2)
  1. The four constraint categories are formally defined but would benefit from one or two concrete examples placed in the main text rather than deferred to an appendix.
  2. Exact evaluation metrics (e.g., plan validity, executability, or formal-language success rate) and the precise prompting templates used for each LLM/formalizer combination should be stated explicitly in the main body or a dedicated subsection.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will incorporate the suggested analyses to strengthen the empirical claims.

read point-by-point responses

  1. Referee: [§3] §3 (Annotation and Constraint Categories): The manuscript provides no quantitative validation of the manual annotations (e.g., inter-annotator agreement, distribution of constraint length or syntactic complexity versus the original instances, or ablation removing individual categories). Without these controls it remains possible that the observed performance halving partly reflects harder or noisier test instances rather than fragility to the intended constraint semantics alone; this directly affects the load-bearing claim that the drop measures robustness.

    Authors: We agree that quantitative validation of the annotations is important for isolating the effect of constraint semantics. In the revision we will add inter-annotator agreement computed on a re-annotated sample, report distributions of constraint length and syntactic complexity relative to the original instances, and include an ablation that removes one category at a time while measuring the resulting performance change. revision: yes

  2. Referee: [§4] §4 (Experimental Results): The paper states that performance is “consistently halved” but reports neither per-instance metrics, statistical significance tests, nor confidence intervals on the deltas. This omission prevents readers from assessing whether the halving is uniform or driven by a subset of instances or categories.

    Authors: We acknowledge the value of statistical detail. The revised manuscript will include per-instance performance tables in an appendix, paired t-tests or Wilcoxon tests on the deltas with p-values, and 95% confidence intervals on the performance reductions, broken down by model, language, dataset, and constraint category. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical robustness measurement on external benchmarks

full rationale

The paper augments existing planning benchmarks with manually annotated natural-language constraints across four categories and directly measures LLM performance drops on standard planning tasks. No derivations, equations, fitted parameters, or self-referential definitions appear in the central claims. The reported halving of performance is a direct experimental observation from running models on the augmented datasets, not a quantity constructed from or equivalent to the inputs by definition. The work relies on external benchmarks and datasets rather than any load-bearing self-citation chain or uniqueness theorem imported from prior author work. This is a standard empirical evaluation study whose results stand or fall on the quality of the annotations and experimental controls, not on any internal circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the manual constraint annotations and the assumption that the chosen benchmarks plus added constraints better approximate real-world planning scenarios.

axioms (1)
  • domain assumption The four formally defined categories of constraints are representative of fine-grained real-world constraints.
    Invoked when the paper claims the added constraints are rich, fine-grained, and span relevant types.

pith-pipeline@v0.9.0 · 5665 in / 1205 out tokens · 54717 ms · 2026-05-18T09:28:29.541210+00:00 · methodology

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