{"slug": "when-planning-problems-become-hard-for-llms-separating-structural-complexity", "title": "When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability", "summary": "A zero-training delete-relaxation heuristic, h_add, predicted structural planning difficulty across seven STRIPS domains better than learned alternatives, achieving a mean Spearman |ρ| of 0.931 on held-out domains versus 0.707 for ridge regression, 0.657 for object count and 0.400 for the episodic retrieval architecture ARC, according to an arXiv paper (2610.26964v1). To isolate semantic effects, the authors progressively renamed predicates while preserving states, transitions and optimal plans, and valid LLM planning success fell from 67.5% to 40.0% between 0% and 100% renaming, with refusals staying at 0%. The authors conclude that established symbolic heuristics already estimate structural difficulty well, and that improving hybrid LLM-symbolic planning requires modeling LLM behavior sensitive to predicate semantics rather than building more structural predictors.", "body_md": "arXiv:2610.26964v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/when-planning-problems-become-hard-for-llms-separating-structural-complexity", "canonical_source": "https://arxiv.org/abs/2610.26964", "published_at": "2026-10-21 04:00:00+00:00", "updated_at": "2026-10-21 04:20:03.666706+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-agents"], "entities": ["ARC", "STRIPS", "h_add"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/when-planning-problems-become-hard-for-llms-separating-structural-complexity", "markdown": "https://wpnews.pro/news/when-planning-problems-become-hard-for-llms-separating-structural-complexity.md", "text": "https://wpnews.pro/news/when-planning-problems-become-hard-for-llms-separating-structural-complexity.txt", "jsonld": "https://wpnews.pro/news/when-planning-problems-become-hard-for-llms-separating-structural-complexity.jsonld"}}