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

2026-02-06

AI Summary

Current large language model agents primarily rely on post-hoc self-reflection for error correction, lacking the capacity for prospective planning prior to action execution. This work proposes PreFlect, a novel mechanism that shifts reflection from a retrospective to a prospective paradigm: by distilling patterns of planning errors from historical trajectories, PreFlect critically evaluates and optimizes plans before execution, while integrating dynamic runtime replanning to address emerging deviations. Evaluated across multiple complex real-world task benchmarks, the approach significantly enhances overall agent utility, outperforming strong reflective baselines as well as more sophisticated agent architectures.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• prospective reflection
• large language model agents
• self-reflection
• planning errors
• agent planning

Innovation

• prospective reflection
• pre-execution planning
• dynamic re-planning
• agent trajectory distillation
• large language model agents