HomeResearch LibraryTowards Long-Horizon Planning with LLMs: A Survey
surveyChapter 2arXiv · 2024

Towards Long-Horizon Planning with LLMs: A Survey

Chuanneng Sun (Purdue), Songjun Huang (Purdue), Djallel Bouneffouf (IBM Research)

Abstract

We survey the landscape of long-horizon planning with LLMs, covering task decomposition, subgoal generation, plan verification, and execution monitoring. We identify key challenges and promising research directions.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This survey provides the most comprehensive map of the long-horizon planning research landscape. For Zone III practitioners, it identifies the key unsolved problems and the most promising research directions.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Long-horizon planning survey
  • →Challenge taxonomy
  • →Research roadmap

Topics

long-horizon planningsurveytask decompositionplan verification