HomeResearch LibraryEnhancement of long-horizon task planning via active an…
Chapter 2 · 2025

Enhancement of long-horizon task planning via active and passive modification in large language models

Kazuki Hori, Kanata Suzuki, Tetsuya Ogata

Abstract

This study proposes a method for generating complex and long-horizon off-line task plans using large language models (LLMs). It addresses the limitation of simple planning results by enabling the LLM to actively collect missing information through questions and passively refine plans with dialogue examples. The method focuses on sequentially eliminating ambiguities in long-horizon tasks, increasing the information in movement plans, and demonstrating effectiveness through cooking task experiments.

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 paper directly addresses one of the core structural challenges in Zone III deployments. The research on LLM, long-horizon task planning, active modification provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

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.

Topics

LLMlong-horizon task planningactive modificationpassive modificationrobotics