SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks

Computer Science > Artificial Intelligence [Submitted on 13 May 2026] SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks Yusuke Ozaki, Dhaval Patel Industrial LLM agent systems often separate planning from execution, yet LLM planners frequently produce structurally invalid or unnecessarily long workflows, leading to brittle failures and avoidable tool and API cost. We propose \texttt{SPIN}, a planning wrapper that combines validated Directed Acyclic Graph (DAG) planning with prefix based execution control. \texttt{SPIN} enforces a strict DAG contract through \texttt{\_validate\_plan\_text} and repair prompting, producing executable plans before downstream execution, and then evaluates DAG prefixes incrementally to stop when the current prefix is sufficient to answer the query. On AssetOpsBench, across 261 scenarios, \texttt{SPIN} reduces executed tasks from 1061 to 623 and improves \emph{Accomplished} from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench, the same wrapper improves planning, grounding, and dependency related scores for both GPT OSS1 and Llama 4 Maverick. Comments: 31 pages, 10 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.14051 [cs.AI]   (or arXiv:2605.14051v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.14051 Focus to learn more Submission history From: Yusuke Ozaki [view email] [v1] Wed, 13 May 2026 19:12:24 UTC (1,523 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)