PreFlect: From Retrospective to Prospective Reflection in Large Language Model Agents

TL;DR

PreFlect introduces a proactive reflection mechanism for large language model agents that anticipates and refines plans before execution, leading to improved performance on complex tasks.

Contribution

It proposes a novel prospective reflection approach that criticizes and refines plans pre-execution, supported by historical error distillation and dynamic re-planning.

Findings

PreFlect outperforms existing reflection-based baselines.

It significantly improves agent utility on real-world tasks.

The method effectively anticipates and corrects planning errors.

Abstract

Advanced large language model agents typically adopt self-reflection for improving performance, where agents iteratively analyze past actions to correct errors. However, existing reflective approaches are inherently retrospective: agents act, observe failure, and only then attempt to recover. In this work, we introduce PreFlect, a prospective reflection mechanism that shifts the paradigm from post hoc correction to pre-execution foresight by criticizing and refining agent plans before execution. To support grounded prospective reflection, we distill planning errors from historical agent trajectories, capturing recurring success and failure patterns observed across past executions. Furthermore, we complement prospective reflection with a dynamic re-planning mechanism that provides execution-time plan update in case the original plan encounters unexpected deviation. Evaluations on different benchmarks demonstrate that PreFlect significantly improves overall agent utility on complex real-world tasks, outperforming strong reflection-based baselines and several more complex agent architectures. Code will be updated at https://github.com/wwwhy725/PreFlect.