arxiv: 2604.08712 · v3 · submitted 2026-04-09 · 💻 cs.AI

Recognition: unknown

Model Space Reasoning as Search in Feedback Space for Planning Domain Generation

James Oswald , Daniel Obolensky , Volodymyr Varha , Vasilije Dragovic , Kavitha Srinivas , Harsha Kokel , Michael Katz , Shirin Sohrabi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:49 UTC · model grok-4.3

classification 💻 cs.AI

keywords planning domain generationheuristic searchmodel spacesymbolic feedbacklarge language modelsagentic frameworkslandmarksVAL validator

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The pith

An agentic LLM framework performs heuristic search over model space using symbolic feedback to generate planning domains from natural language.

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

The paper frames planning domain generation as a search task in which an agentic language model explores candidate domains starting from natural language descriptions plus minimal symbolic input. It tests whether feedback signals such as extracted landmarks and output from the VAL plan validator can steer the search toward higher-quality domains that pass validation and support correct plans. A sympathetic reader would care because hand-authoring planning domains is labor-intensive and currently limits the deployment of automated planners in new settings. If the approach works, it would let users obtain working domains by describing problems in ordinary language rather than writing formal specifications from scratch. Experiments compare the effect of different feedback types on the search process and resulting domain performance.

Core claim

The authors claim that heuristic search over the space of possible planning domains, directed by symbolic feedback including landmarks and VAL plan validator output, enables an agentic language model to produce domains of sufficient quality for practical use from natural language descriptions augmented with only a small amount of symbolic information.

What carries the argument

Heuristic search over model space in which each state is a candidate planning domain and transitions are driven by symbolic feedback from landmarks and VAL validator results.

If this is right

Including landmarks in the feedback improves the ability of search to reach domains that generate valid plans.
VAL validator output supplies concrete error signals that the search process can use to correct domain flaws.
A small amount of symbolic augmentation added to natural language is enough to bootstrap effective search-based domain generation.
The resulting domains support plan generation that passes independent validation checks in automated planners.

Where Pith is reading between the lines

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

The same search-in-feedback-space pattern could be tested on generating formal models for other tasks such as workflow specification or constraint satisfaction problems.
One could measure whether adding cost or resource feedback signals beyond landmarks further speeds convergence to usable domains.
The method suggests treating LLM output as starting points for iterative search rather than final artifacts when the target is a formal structure.

Load-bearing premise

The chosen symbolic feedback signals from landmarks and VAL output supply enough reliable guidance to make search in model space effective, and LLMs can incorporate this feedback without introducing unrecoverable errors.

What would settle it

The central claim would be falsified if domains produced after search iterations with landmarks and VAL feedback fail to yield valid plans under VAL validation at rates no better than those from direct LLM prompting without search or feedback.

Figures

Figures reproduced from arXiv: 2604.08712 by Daniel Obolensky, Harsha Kokel, James Oswald, Kavitha Srinivas, Michael Katz, Shirin Sohrabi, Vasilije Dragovic, Volodymyr Varha.

**Figure 2.** Figure 2: HDE score averaged across all domains for each model and feedback pipeline. Error bars [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high quality domains that can be deployed in practice. To this end, we investigate the ability of an agentic language model feedback framework to generate planning domains from natural language descriptions that have been augmented with a minimal amount of symbolic information. In particular, we evaluate the quality of the generated domains under various forms of symbolic feedback, including landmarks, and output from the VAL plan validator. Using these feedback mechanisms, we experiment using heuristic search over model space to optimize domain quality.

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.

Desk Editor's Note private letter to a colleague

The paper frames planning domain generation as heuristic search in model space with an agentic LLM guided by landmarks and VAL feedback, which structures the problem sensibly but provides no results to show whether it works better than prior LLM attempts.

read the letter

The main thing to know is that this work casts domain generation as search over possible models, where the LLM agent iterates using feedback from landmark detection and the VAL validator on minimally augmented natural language inputs. That framing is new compared to earlier LLM-only efforts, and it makes sense as a way to bring in independent symbolic signals instead of trusting the model alone. The paper does a solid job describing the overall loop and laying out experiments with different feedback combinations, which could give the planning community a clearer way to think about hybrid refinement steps. The use of external validators like VAL avoids some of the circularity that pure LLM self-critique often runs into. The soft spot is the complete lack of any quantitative results, baselines, or details on how domain quality gets measured. Without those, it is impossible to tell if the search actually produces deployable domains or if the LLM reliably incorporates the feedback without introducing new errors. The central assumption that landmarks and VAL output will supply enough reliable guidance stays plausible but untested here. This paper is aimed at researchers working on automated planning and LLM-symbolic hybrids, especially anyone dealing with the domain acquisition bottleneck in robotics or operations research. A reader looking for concrete ideas on feedback mechanisms would get value from the framework even if the experiments are still preliminary. It deserves a serious referee because the topic is relevant and the method is clearly defined, though any review would focus on adding the missing evaluation. I recommend sending it to peer review with a request for results and comparisons.

Referee Report

1 major / 2 minor

Summary. The paper claims that an agentic LLM framework can generate deployable planning domains from natural language descriptions (augmented with minimal symbolic information) by framing domain generation as heuristic search over model space, where search is guided by symbolic feedback signals including detected landmarks and output from the VAL plan validator.

Significance. If the empirical claims hold, the work offers a structured neuro-symbolic approach to a longstanding open problem in automated planning, potentially improving the reliability of LLM-generated PDDL domains beyond current direct-generation baselines. The explicit use of external validators and landmarks as feedback provides a falsifiable mechanism that could generalize to other formal synthesis tasks.

major comments (1)

[Experimental evaluation] The manuscript provides no quantitative results, baseline comparisons, or explicit definition of the domain-quality metric used to guide or evaluate the search (see abstract and any experimental section). This absence prevents assessment of whether the proposed feedback-driven search actually yields deployable domains or outperforms simpler LLM prompting.

minor comments (2)

Clarify the precise state representation used for model-space search and how the heuristic is computed from the symbolic feedback signals.
The abstract would be strengthened by a single sentence summarizing the key quantitative outcome (e.g., success rate or plan validity improvement).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the potential of our neuro-symbolic approach to the open problem of planning domain generation. We agree that the current manuscript requires a substantially strengthened experimental evaluation section with quantitative results, baseline comparisons, and an explicit domain-quality metric. We will revise accordingly to address this major comment.

read point-by-point responses

Referee: [Experimental evaluation] The manuscript provides no quantitative results, baseline comparisons, or explicit definition of the domain-quality metric used to guide or evaluate the search (see abstract and any experimental section). This absence prevents assessment of whether the proposed feedback-driven search actually yields deployable domains or outperforms simpler LLM prompting.

Authors: We acknowledge the validity of this observation. Although the abstract describes evaluation under symbolic feedback (landmarks and VAL output) and heuristic search over model space, the experimental section in the submitted manuscript is preliminary and lacks the requested quantitative details. In the revised version we will: (1) define the domain-quality metric explicitly as a composite score combining VAL validation success rate, landmark coverage, and plan executability; (2) report quantitative results including success rates, average search iterations to convergence, and domain-quality scores across a set of natural-language-to-PDDL tasks; and (3) include direct baseline comparisons against simple LLM prompting (no feedback, no search) and against search without symbolic feedback. These additions will enable a clear assessment of whether the feedback-driven search improves deployability over simpler methods. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external validators

full rationale

The paper presents an agentic LLM framework performing heuristic search over model space, guided by independent external symbolic feedback (landmarks and VAL plan validator output). These components are standard, pre-existing tools from the planning literature and not constructed from the paper's own fitted parameters, self-definitions, or prior self-citations. No load-bearing step reduces by construction to the inputs; the quality optimization is driven by verifiable external signals rather than internal renaming or ansatz smuggling. The approach is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on domain assumptions about LLM responsiveness to symbolic feedback and the tractability of search in model space; no free parameters or invented entities are specified in the abstract.

axioms (2)

domain assumption LLMs can be guided by symbolic feedback signals such as landmarks and validator output to produce higher-quality planning domains
Invoked by the agentic feedback framework described in the abstract.
domain assumption Heuristic search over the space of possible models can optimize domain quality when driven by the chosen feedback
Stated in the use of heuristic search over model space to optimize domain quality.

pith-pipeline@v0.9.0 · 5437 in / 1279 out tokens · 38628 ms · 2026-05-10T16:49:43.602592+00:00 · methodology

discussion (0)

Reference graph

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overall": {

11 ICLR 2026 the 2nd Workshop on World Models: Understanding, Modelling and Scaling A SAMPLEPDDL DOMAIN ANDDESCRIPTION Here we provide our version of the classic blocksworld domain, referred to in Tables 2 and 1 as “blocks”. (define (domain blocks) (:requirements :strips :typing) (:types block) (:predicates (on ?x - block ?y - block) (ontable ?x - block) ...

2026