AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
[Submitted on 18 Oct 2026] Title:When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability View a PDF of the paper titled When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability, by Mayukh Debnath and 2 other authors View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability, by Mayukh Debnath and 2 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI new | recent | 2026-10 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)