[Submitted on 18 Oct 2026]
Title:When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
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Abstract:Large language models (LLMs) struggle with classical planning, but benchmarks rarely distinguish structural difficulty from the difficulty of interpreting symbolic vocabulary. We examine seven STRIPS domains with BFS-optimal plan lengths. For predicting structural difficulty without solving an instance, the zero-training delete-relaxation heuristic $h_{\mathrm{add}}$ outperforms learned alternatives on held-out domains (mean Spearman $|\rho|=0.931$), including ridge regression ($0.707$), object count ($0.657$), and the episodic retrieval architecture ARC ($0.400$). ARC also fails to outperform a linear model using the same features. To isolate semantic effects, we progressively rename predicates while preserving states, transitions, and optimal plans. Valid LLM planning success drops from $67.5\%$ to $40.0\%$ between $0\%$ and $100\%$ renaming, while refusals remain at $0\%$. An LLM supplied with ARC's conditioning information predicts difficulty in context better than trained ARC (mean $|\rho|=0.826$). Hybrid routing between an LLM and a symbolic planner depends more on calibration to the target LLM's outcomes than on the structural score selected. Thus, established symbolic heuristics already estimate structural difficulty well; improving hybrid planning requires modeling LLM behavior sensitive to predicate semantics rather than developing more structural predictors.
Comments: 10 pages, 1 figure, 17 supplementary pages
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26964 [cs.AI]
(or arXiv:2610.26964v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26964
arXiv-issued DOI via DataCite
Submission history
From: Adeyinka Adedigba [view email] [v1] Sun, 18 Oct 2026 15:45:30 UTC (836 KB)
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