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arxiv: 2605.29649 · v2 · pith:SAWUXQ3Lnew · submitted 2026-05-28 · 💻 cs.AI

LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning

Elliot Gestrin , Jendrik Seipp This is my paper

Pith reviewed 2026-06-29 07:25 UTC · model grok-4.3

classification 💻 cs.AI
keywords heuristic searchAI planninglarge language modelsevolutionary algorithmsdomain-independent heuristicsMAP-Elitessymbolic planninginformedness-speed tradeoff
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The pith

LLM-driven evolution on training domains produces the first domain-independent heuristics that solve more tasks than the strongest hand-engineered baselines on entirely new domains.

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

The paper evolves C++ heuristic functions for symbolic planning by letting an LLM mutate existing programs, archiving variants in a grid sorted by how informative versus fast each one is, and scoring them on a mix of how many tasks they finish and how long they take. Training happens only on a fixed collection of domains; the resulting programs are then tested on held-out domains never seen during evolution. A sympathetic reader would care because heuristic quality has been the main limit on what symbolic planners can solve, and an automated route that beats decades of expert design would change how such systems are built. The programs remain ordinary C++ so they drop straight into existing planners and keep their formal guarantees.

Core claim

By seeding an evolutionary loop with an LLM that proposes mutations to parent heuristics written in C++, maintaining a MAP-Elites archive indexed by informedness and speed, and assigning fitness from a blend of coverage and runtime on training domains, the method yields heuristics whose best member solves more tasks than the strongest baseline on unseen test domains while the full collection of evolved programs spans the Pareto frontier of the informedness-speed tradeoff.

What carries the argument

MAP-Elites archive keyed on informedness and speed, with LLM mutations generating new C++ heuristic candidates whose fitness is computed from coverage blended with solving time on training domains.

If this is right

  • The evolved heuristics slot into existing planners as ordinary C++ functions and inherit soundness and completeness.
  • Seeding the evolutionary process from the blind heuristic can produce stronger final programs than seeding from the FF heuristic.
  • LLM reasoning effort during mutation affects how often candidates compile far more than the quality of the candidates that do compile.
  • A broad benchmark of hand-engineered heuristics on the informedness-speed tradeoff is now available for comparison.

Where Pith is reading between the lines

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

  • The same mutation-plus-archive loop could be applied to other search problems that rely on hand-crafted heuristics, such as game playing or verification.
  • Planners could dynamically select among the Pareto-optimal evolved heuristics according to current time or memory limits.
  • Varying the diversity or number of training domains would test how robust the generalization is to changes in the training distribution.
  • The plain C++ form opens the possibility of further manual or automated refinement after evolution.

Load-bearing premise

Heuristics evolved only on a finite set of training domains will generalize their performance advantage to completely new planning domains without overfitting to the training distribution or the particular coverage-runtime fitness blend.

What would settle it

A collection of new planning domains in which none of the evolved heuristics solves more tasks than the strongest hand-engineered baseline would falsify the generalization result.

Figures

Figures reproduced from arXiv: 2605.29649 by Elliot Gestrin, Jendrik Seipp.

Figure 2
Figure 2. Figure 2: Cactus plot on the held-out 2023 IPC Learning [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Per-run trajectories of the best-so-far training score (left axis) alongside a kernel-density estimate of improvement [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of scores grouped by reasoning effort. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cactus plot on the 2023 IPC Learning Track test instances with the four notable evolved heuristics from Section 5 [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Unfiltered informedness–speed scatter across all heuristics in our benchmark, including the weak baselines that [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Stacked task outcomes per heuristic on the held-out test set: solved, out of time, out of memory, translator out of [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: For each ordered pair of heuristics, the share of test tasks that the row solves and the column does not. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Jaccard similarity between the per-heuristic sets of solved tasks from Figure 7. High values mark heuristics that are [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of training scores at each generation across all evolved runs. Later generations trend toward higher scores, [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
read the original abstract

Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers. Recent work has shown that large language models (LLMs) can design heuristics for individual planning domains, but no LLM-generated heuristic has so far worked on arbitrary planning tasks. In this paper, we use evolutionary search to produce the first LLM-generated domain-independent heuristics that exceed the hand-engineered state of the art. We let an LLM mutate parent heuristics written in C++, store candidates in a MAP-Elites archive keyed on informedness and speed and calculate fitness scores by blending coverage with solving time. To place the evolved programs in context, we additionally benchmark a broad set of hand-engineered heuristics on their informedness-speed tradeoff, which to our knowledge has not been done before. On unseen testing domains, our best evolved heuristic solves more tasks than even the strongest baseline, with our full heuristic suite spanning the Pareto frontier of said tradeoff. We also find that seeding evolution from the trivial blind heuristic outperforms seeding from the strong FF heuristic, even when the resulting program is itself an FF variant, and that LLM reasoning effort affects how often candidates compile much more than the quality of those that do. Because the evolved programs are plain C++, they slot into existing planners as drop-in replacements and inherit the soundness and completeness guarantees of the underlying search.

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

3 major / 2 minor

Summary. The paper claims to introduce the first LLM-generated domain-independent heuristics for symbolic AI planning that outperform hand-engineered state-of-the-art baselines. It uses evolutionary search where an LLM mutates C++ heuristic code, stores candidates in a MAP-Elites archive keyed on informedness and speed, and computes fitness by blending coverage with solving time. On unseen test domains, the best evolved heuristic reportedly solves more tasks than the strongest baseline, with the full suite spanning the informedness-speed Pareto frontier. Additional results include that seeding from the blind heuristic outperforms seeding from FF, and that LLM reasoning effort primarily affects compilation rates rather than quality of successful candidates. The evolved C++ programs are presented as drop-in replacements inheriting planner guarantees.

Significance. If the generalization results hold under rigorous evaluation, this would represent a notable advance by showing that LLM-driven evolution can produce domain-independent planning heuristics exceeding decades of hand-engineered work, with practical integration via C++ code. The broad benchmarking of hand-engineered heuristics on the informedness-speed tradeoff is a useful and previously unreported contribution that provides context for the evolved results.

major comments (3)
  1. [Experimental Evaluation] Experimental Evaluation (likely §4 or equivalent): The central claim that the best evolved heuristic outperforms the strongest baseline on unseen testing domains, and that the suite spans the Pareto frontier, is presented without any details on the number of training vs. testing domains, the procedure for creating the train/test split, domain diversity metrics, selection criteria for domains, or any statistical tests (e.g., significance of coverage differences). These omissions directly undermine evaluation of the generalization step highlighted as the weakest assumption.
  2. [Method / Fitness] Fitness computation description (likely §3): The fitness score is described as a blend of coverage and solving time, but the specific weights, how they were chosen, and any sensitivity analysis are not reported. This is load-bearing because the MAP-Elites archive and Pareto claims depend on this blending, and different weights could alter which candidates are retained or which evolved heuristic is deemed best.
  3. [Results] Results on seeding and LLM effort (likely §5): Claims that seeding from the blind heuristic outperforms FF seeding (even for FF-variant outputs) and that reasoning effort affects compilation more than quality lack reported run counts, variance across runs, or controls for other factors, making it impossible to assess whether these are robust findings supporting the overall approach.
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction could more explicitly reference the specific tables or figures showing the Pareto frontier comparison to allow readers to locate the supporting data immediately.
  2. [Method] Notation for informedness and speed metrics in the MAP-Elites archive could be defined more formally with equations or pseudocode to improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which identify areas where additional clarity and rigor will strengthen the presentation of our results. We respond to each major comment below and will incorporate the suggested improvements in the revised manuscript.

read point-by-point responses

  1. Referee: [Experimental Evaluation] Experimental Evaluation (likely §4 or equivalent): The central claim that the best evolved heuristic outperforms the strongest baseline on unseen testing domains, and that the suite spans the Pareto frontier, is presented without any details on the number of training vs. testing domains, the procedure for creating the train/test split, domain diversity metrics, selection criteria for domains, or any statistical tests (e.g., significance of coverage differences). These omissions directly undermine evaluation of the generalization step highlighted as the weakest assumption.

    Authors: We agree that the experimental section requires expanded reporting to allow readers to assess the generalization claims. In the revision we will add: the precise counts of training and testing domains, the exact procedure used to form the train/test split (including any stratification or diversity considerations), quantitative domain diversity metrics, the domain selection criteria, and statistical tests (such as Wilcoxon signed-rank tests) with p-values for coverage differences between the best evolved heuristic and the strongest baseline. These details will be placed in a dedicated subsection of the experimental evaluation. revision: yes

  2. Referee: [Method / Fitness] Fitness computation description (likely §3): The fitness score is described as a blend of coverage and solving time, but the specific weights, how they were chosen, and any sensitivity analysis are not reported. This is load-bearing because the MAP-Elites archive and Pareto claims depend on this blending, and different weights could alter which candidates are retained or which evolved heuristic is deemed best.

    Authors: We acknowledge that the fitness function description is incomplete. The revised manuscript will explicitly state the weights applied to coverage and solving time, explain the rationale and any preliminary experiments used to select them, and include a sensitivity analysis that varies the weights and reports the resulting changes to the retained archive members and the identity of the best heuristic. This information will be added to Section 3. revision: yes

  3. Referee: [Results] Results on seeding and LLM effort (likely §5): Claims that seeding from the blind heuristic outperforms FF seeding (even for FF-variant outputs) and that reasoning effort affects compilation more than quality lack reported run counts, variance across runs, or controls for other factors, making it impossible to assess whether these are robust findings supporting the overall approach.

    Authors: We agree that the robustness of the seeding and LLM-effort results needs better documentation. The revision will report the number of independent runs performed for each condition, means and standard deviations (or other variance measures) across runs, and any controls applied for confounding factors. These statistics will be added to the relevant results subsection. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmarking against external baselines

full rationale

The paper reports an experimental protocol that evolves C++ heuristics via LLM mutation on a finite training-domain set, then measures coverage and runtime on a disjoint unseen test set, comparing directly to a broad suite of hand-engineered baselines. No equations, fitted parameters, or self-referential definitions appear; the reported advantage is a measured outcome, not a quantity forced by construction from the training fitness function or archive. Self-citations (if any) are not load-bearing for the central empirical claim, which remains falsifiable by external replication on the same domain split.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on the abstract alone, the central claim rests on the standard assumption that heuristic search remains sound when the heuristic is admissible or consistent, plus the empirical assumption that the chosen training domains and fitness blend produce generalizable programs. No free parameters or invented entities are explicitly named.

axioms (1)
  • standard math Heuristic search algorithms remain sound and complete when supplied with admissible or consistent heuristics.
    Implicit background assumption of all symbolic planning work; invoked when the abstract states that the evolved C++ programs inherit the guarantees of the underlying search.

pith-pipeline@v0.9.1-grok · 5773 in / 1313 out tokens · 28652 ms · 2026-06-29T07:25:17.910803+00:00 · methodology

discussion (0)

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