mlsanityData to 7 Oct 2026

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

arXiv 2602.07187 ↗First posted 6 Feb 2026cs.AIcited by 4

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 34 of its 50 references that are in the map. First posted on arXiv 6 Feb 2026.

Builds on

34 of 50 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection24 Sep
  2. 2025Efficient Agents: Building Effective Agents While Reducing Cost24 Jul
  3. 2025OAgents: An Empirical Study of Building Effective Agents17 Jun
  4. 2025OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation29 May
  5. 2025Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution26 May
  6. 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks7 Nov
  7. 2024Devil's Advocate: Anticipatory Reflection for LLM Agents25 May
  8. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  9. 2023AutoAgents: A Framework for Automatic Agent Generation29 Sep

Other references · 25

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  6. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  7. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  8. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  9. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  10. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  11. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  12. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
13 more
  1. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  2. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  3. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  4. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
  5. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 423
  6. 2024Measuring short-form factuality in large language modelscited by 345
  7. 2025ToolRL: Reward is All Tool Learning Needscited by 342
  8. 2025Where LLM Agents Fail and How They can Learn From Failurescited by 118
  9. 2024Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimizationcited by 112
  10. 2025VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Usecited by 92
  11. 2025Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratchcited by 17
  12. 2025TruthFlow: Truthful LLM Generation via Representation Flow Correctioncited by 14
  13. 2025MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learningcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestrationcited by 11
  2. 2026Retrospective Progress-Aware Self-Refinement for LLM Agent Trainingcited by 6
  3. 2026RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinementcited by 4
  4. 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024PreAct: Prediction Enhances Agent's Planning Ability
  2. 2026LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation
  3. 2026Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents
  4. 2025Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
  5. 2026OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions
  6. 2026OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
  7. 2025Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions
  8. 2026EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective