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system architectureChapter 2EACL 2024 · 2023

PEARL: Prompting Large Language Models to Plan and Execute Actions for Long-Horizon Tasks

Simeng Sun (UMass Amherst), Yang Liu (Salesforce Research)

Abstract

We present PEARL, a prompting framework for long-horizon task planning and execution. PEARL decomposes tasks into action plans, executes them step by step, and self-evaluates progress.

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)

PEARL's plan-execute-evaluate loop is the core pattern for Zone III workflow execution. The explicit self-evaluation step is particularly important — it provides the feedback signal needed to detect and correct errors before they compound.

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

  • →Plan-execute-evaluate loop
  • →Long-horizon task decomposition
  • →Self-evaluation integration

Topics

long-horizon planningaction executionself-evaluationprompting