{"version":1,"type":"story","url":"https://digestai.news/story/llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform","json":"https://digestai.news/story/llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform.json","markdown":"https://digestai.news/story/llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform.md","slug":"llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform","headline":"LLMs struggle with planning problems as symbolic heuristics outperform learned models","summary":"A new arXiv study shows that large language models (LLMs) have difficulty solving classical planning tasks, and that traditional symbolic heuristics can predict problem difficulty better than learned approaches. The zero‑training delete‑relaxation heuristic \\(h_{add}\\) reaches a mean Spearman correlation of 0.931 on held‑out STRIPS domains, beating ridge regression (0.707), simple object counting (0.657) and the episodic retrieval architecture ARC (0.400). ARC also fails to surpass a linear model that uses the same features.\n\nTo isolate the effect of semantic understanding, the researchers progressively renamed predicates while keeping states, transitions and optimal plans unchanged. LLM planning success falls from 67.5% to 40.0% as renaming moves from 0% to 100%, with refusal rates staying at 0%. Supplying an LLM with ARC’s conditioning information improves difficulty prediction (mean \\(|\\rho|=0.826\\)) compared with a trained ARC model. The paper concludes that improving hybrid LLM‑symbolic planners requires better modeling of LLM behavior sensitive to predicate semantics rather than creating new structural difficulty predictors.","keyPoints":["Zero‑training delete‑relaxation heuristic h_add achieves 0.931 mean Spearman correlation, outpacing ridge regression (0.707), object count (0.657) and ARC (0.400).","Renaming all predicates drops LLM planning success from 67.5% to 40.0% while refusal rate remains at 0%.","Hybrid LLM‑symbolic planning benefits more from calibrating to LLM outcomes than from choosing a structural difficulty predictor."],"whyItMatters":"The findings reveal that LLMs are more limited by semantic interpretation than structural complexity, guiding future hybrid planning systems and research on integrating symbolic heuristics with language models.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":["ARC"],"people":[]},"firstPublishedAt":"2026-10-21T04:00:00Z","updatedAt":"2026-10-21T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.AI","title":"When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability","url":"https://arxiv.org/abs/2610.26964","publishedAt":"2026-10-21T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"LLMs struggle with planning problems as symbolic heuristics outperform learned models\", 21 October 2026, https://digestai.news/story/llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform","publisher":"Digest AI","title":"LLMs struggle with planning problems as symbolic heuristics outperform learned models","datePublished":"2026-10-21T04:00:00Z","url":"https://digestai.news/story/llms-struggle-with-planning-problems-as-symbolic-heuristics-outperform"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}